RBI Bulletin - Nov 24, 2025

NOVEMBER 2025 VOLUME LXXIX NUMBER 11Editorial Committee Sanjay Kumar Hansda Anujit Mitra Rekha Misra Anupam Prakash Sunil Kumar Rajeev Jain Snehal Herwadkar V. Dhanya Shweta Kumari Anirban Sanyal Sujata Kundu Editor Asish Thomas George The Reserve Bank of India Bulletin is issued monthly by the Department of Economic and Policy Research, Reserve Bank of India, under the direction of the Editorial Committee. The Central Board of the Bank is not responsible for interpretation and opinions expressed. In the case of signed articles, the responsibility is that of the author. © Reserve Bank of India 2025 All rights reserved. Reproduction is permitted provided an acknowledgement of the source is made. For subscription to Bulletin, please refer to Section ‘Recent Publications’ The Reserve Bank of India Bulletin can be accessed at https://bulletin.rbi.org.inCONTENTS Speech Regulation by RBI: Some Reflections by Shri Sanjay Malhotra 1 The Evolving Facets of Regulations by Shri Sanjay Malhotra 9 Transformational Technologies and Banking: Key Issues by 15 Shri T Rabi Sankar Where Governance Intent is Strong, Regulatory Gaps and Overlaps 19 Fade by Shri Swaminathan J Policy Frameworks for Economic Resilience: The Case of Emerging 23 Markets and India by Dr. Poonam Gupta Central Bank Accounting Practices: The Reserve Bank of India and 27 Global Approaches by Shri Shirish Chandra Murmu Articles State of the Economy 33 ‘Making the Horizons Meet’: A Heterodox Approach for Short-Term 63 Inflation Forecasting Multivariate Core Trend Inflation: A New Measure of Core Inflation 73 Nowcasting GDP in India: A New Approach 87 Seasonality in Key Economic Indicators of India 103 Current Statistics 129 Recent Publications 185SPEECH Regulation by RBI: Some Reflections by Shri Sanjay Malhotra The Evolving Facets of Regulations by Shri Sanjay Malhotra Transformational Technologies and Banking: Key Issues by Shri T Rabi Sankar Where Governance Intent is Strong, Regulatory Gaps and Overlaps Fade by Shri Swaminathan J Policy Frameworks for Economic Resilience: The Case of Emerging Markets and India by Dr. Poonam Gupta Central Bank Accounting Practices: The Reserve Bank of India and Global Approaches by Shri Shirish Chandra MurmuRegulation by RBI: Some Reflections SPEECH Regulation by RBI: rigorous selection process. I have been thinking about you because you are the future leaders of our country. Some Reflections* I have been pondering about what I should speak to you bright men and women. India looks up to you Shri Sanjay Malhotra to shape and influence public policy and economic research in our country, as many of your predecessors Prof. Ram Singh, Director, Delhi School of have done. Economics, Prof. Pami Dua, distinguished faculty of Considering the erudite audience, I have chosen the Delhi School of Economics, assembled dignitaries to speak on regulation making, because regulations and dear students. Good afternoon. are pervasive. They represent an invisible fabric that I am pleased to be here at the Delhi School of enables markets to function, protects consumers, and Economics (DSE) to deliver the Second V.K.R.V. Rao maintains the delicate balance between innovation Memorial lecture. The late Professor Rao was not only and stability. I have structured my speech into five a distinguished scholar - being one of the first three parts – (i) the role of regulation in public policy, (ii) Ph.Ds in Economics from Cambridge University and how financial regulation is different and critical, (iii) winning the prestigious Adam Smith Prize in 1935 RBI’s objectives of regulation, (iv) RBI’s key principles - but also an eminent institution builder. He served of regulation, and finally (v) some challenges in as member of the Planning Commission and Union regulation making. I hope this will trigger some Education Minister. For his outstanding contribution interest for research in this area among the students to public policy and economic research, Professor Rao and faculty. was awarded the Padma Vibhushan in 1974. It is an honour to deliver a lecture in the memory of such a I. Role of Regulation in Public Policy distinguished personality. You are all aware that while markets are powerful DSE is an august institution that has made stellar engines of growth and efficiency, they can fail and contributions in the economic development of our when they do, the consequences can be severe. country. The people who have studied, researched, or Regulations attempt to address market failures caused taught here have had profound influence in shaping due to a variety of reasons: information asymmetry, economic policy in India over the years. We, at the externalities, natural monopolies, systemic risks, and Reserve Bank of India too have benefited immensely consumer protection, to name a few. Regulation is a given that many students have joined the Bank. Many pragmatic response to the inherent limitations of of them have risen to the upper echelons of the Bank’s unregulated markets in specific contexts. management over the years. I thank Prof. Ram Singh II. Financial Regulation: A Different Paradigm and DSE for giving me this opportunity to address you Financial regulation operates in a fundamentally all at this prestigious institution. different framework compared to other sectoral You all have been in my thoughts for the last few regulations. This difference stems from three unique days. You are all very bright and intelligent, having got characteristics of financial markets. admission to this prestigious institution, through a First, financial institutions are interconnected in * Lecture delivered by Shri Sanjay Malhotra, Governor, Reserve Bank ways that non-financial entities rarely are. If a bank of India at the Second V.K.R.V. Rao Memorial Lecture, Delhi School of Economics, New Delhi, November 20, 2025 fails, it has a cascading effect - depositors lose savings, RBI Bulletin November 2025 1SPEECH Regulation by RBI: Some Reflections inter-bank markets freeze, credit supply contracts, market infrastructure – is capable of withstanding and payment systems falter. This impacts the entire shocks and the unravelling of financial imbalances, economy, which can feed into a systemic crisis. The thereby mitigating the likelihood of disruptions in 2008 global financial crisis in the Advanced Economies the financial intermediation process which are severe (AEs) demonstrated this with devastating clarity. enough to significantly impair the allocation of savings to profitable investment opportunities”1. Second, financial institutions are inherently fragile due to maturity and liquidity transformation. For us in the Reserve Bank, financial stability Banks accept short-term deposits and make long-term remains the north star, because we realise that short loans. This transformation is economically valuable term growth achieved at the cost of financial stability but creates vulnerability. A loss of confidence can can have bigger consequences for long-term growth. trigger bank runs, converting liquidity problems into Research shows that financial instability may not only more than offset the gains of higher short-term solvency crises within days. Unlike a manufacturing growth, but also make recovery more distressful and unit that can be shut down temporarily, a bank facing longer. a run must be resolved immediately, or contagion spreads. Although financial stability remains the bedrock, there are other objectives, occasionally overlapping, Third, financial markets are prone to procyclicality yet distinct. These, inter alia, include: and herd behaviour. During booms, risk is under- priced, lending standards deteriorate, and asset a. Prudential aspects, for example liquidity bubbles form. During busts, credit vanishes precisely and capital requirements, to ensure safety when it is most needed. This amplification of and soundness of financial operations in business cycles distinguishes financial markets from the interest of all stakeholders, especially, most other sectors. The procyclical behaviour and its depositors; amplification effects on economic volatility are well b. Conduct related measures for consumer recognised. protection; These characteristics explain why financial c. Assistance in law enforcement, e.g., regulation is more complex, and more consequential prevention of money laundering; and than regulation in other sectors. It is not merely about d. Broad socio-economic objectives, e.g., lending protecting individual consumers and promoting to priority sectors. efficiency - though these are of paramount importance - but also about safeguarding systemic stability and, by IV. RBI’s Key Principles of Financial Regulation extension, the entire economy. As I mentioned in one of my past speeches2, III. RBI’s Objectives of Regulation there are five key principles of our regulation making: preference for principle-based formulation; I will now spell out RBI’s main objectives of proportionality; consultation; evidence and data- regulations. based; and lastly, regular review. Let me briefly touch The foremost priority and key objective is to ensure upon these principles to elaborate their importance. financial stability in the system. Financial Stability is defined as a “condition in which the financial system 1 Financial Stability Review, ECB (December 2006) 2 Inaugural Address by Shri Sanjay Malhotra, Governor, Reserve Bank of – comprising of financial intermediaries, markets and India at the FIBAC 2025 Conference, Mumbai, August 25, 2025 2 RBI Bulletin November 2025Regulation by RBI: Some Reflections SPEECH Preference for Principle-based Regulation vis-a-vis And that’s why you would observe that while we are Rule-based Regulation moving towards a principle-based regulatory regime, most of our regulations are hybrid - a combination First is the idea of principle-based regulation. of rules and principles. The proposed expected Principle-based regulation focuses on outcomes. It credit loss (ECL) framework for provisioning norms, uses high-level general statements or principles. These announced in October this year3, is a good example principles are designed to be applicable across a wide of this evolution. The framework combines principles range of circumstances. It often contains explanations for provisioning with rule-based prudential floors to of the intent behind the principle and qualitative avoid misuse and misinterpretation. rather than quantitative terms. The challenge for us is to achieve “optimal In contrast, rule-based regulation uses specific simplicity” - regulation that is as simple as possible statements to define requirements that entities must but no simpler, to paraphrase Einstein. meet. These necessarily focus on specific areas and Proportionality tend to use quantitative terms. If one was to draw an analogy with parenting, the explicit to-do list made by Coming to the second principle, proportionality, it is parents in early formative age is akin to “rule-based an emerging feature of more rationalised regulatory regulation” and the broad guidance given as the child frameworks across the world. Indian financial system grows older is akin to “principle-based regulation”. comprises a diverse set of financial institutions and banks with varying scales of operation, levels of The global discourse on regulatory simplification, complexity and extent of risks. It is therefore natural argues for principle-based regulations over rule- that our regulations are proportionate in measure. based approaches, for simplicity over exhaustive The principle of proportionality is like a customised specification. The logic is compelling: complex set of risk-sensitive regulations, balancing the costs rules create compliance cultures rather than risk and benefits based on the risk implication of the management cultures. They invite gaming and institutions concerned. arbitrage. Moreover, they become outdated as markets evolve. This fundamental principle reflects duly in our policy formulation across and within groups of Principle-based regulation reduces the potential entities. For example, the idea of proportionality has for ‘creative compliance’, avoids the ‘tick-box been embedded in the regulatory architecture for approach’, and obviates the need for frequent NBFCs in the form of scale-based regulations; tier- updations. However, principle-based regulation, while based structure for cooperative banks; and higher simple, is subject to interpretation risk. Principles capital requirements for domestic systematically without clear standards can lead to inconsistent important banks (D-SIBs). Similarly, proportionality application and regulatory capture. This may also is also reflected in differential treatment of banks significantly increase cost of compliance for regulated as compared to the NBFCs on several parameters entities due to the requirement to form their own including granularity of exposure, liquidity and policies with detailed rationale. capital requirements. Proportionality is kept in We often face a question as to which approach mind even for differential risks perceived by similar is better. There is no straight-forward answer to this, 3 Draft “Reserve Bank of India (Scheduled Commercial Banks-Asset even though we prefer principle-based regulation. Classification, Provisioning and Income Recognition) Directions (Oct 2025) RBI Bulletin November 2025 3SPEECH Regulation by RBI: Some Reflections entities, for example, different capital requirements One, the revised guidelines on project finance, are prescribed for different types of loans and other emerged from a careful study of default rates, recovery exposures keeping in view their differential risks. All rates, and the cash flow characteristics of such lending of these buttresses the point I mentioned earlier that by banks. Two, the increase in the run-off factors for proportionality is a “customised set of risk-sensitive digital deposits under the refined liquidity coverage regulation”. ratio (LCR) requirements for banks demonstrates another example of evidence-based regulation. Three, Consultation we increased the risk weights on bank credit to NBFCs The third principle is consultation. We realise in view of certain emerging risks in this segment post- that triangulation is a must. We appreciate that we do covid; these were subsequently restored to original not have the monopoly for knowledge. Consultation weights, once the evidence suggested so. has been an integral part of our decision making. It Regular Review explicitly embeds accountability and transparency, which are important to us as a public authority. It The fifth element is regular review. Regulations bridges information asymmetry. It enables us to need to change when the context changes. However, look at things through the eyes of others and see there is a stickiness to regulation because of regulators’ many more dimensions. Consultation improves our proclivity to adhere to status quo. A variety of well- regulation making. At the Reserve Bank, we have recognized behavioural phenomena aid in explaining institutionalised this requirement in our framework4. such a bias: common cognitive biases and risk aversion Every major regulation is proposed in draft form for tend to advantage the status quo. In laboratory public consultation. experiments, for instance, psychologists find that framing specific options as the status quo result in Evidence-based Approach those options being selected far more frequently The fourth element is to make regulations than when there is a neutral framing of the options. evidence-based, as far as possible. Evidence- Banking regulators’ behaviour is no different. Claudio based policymaking refers to the method of policy Borio, former senior official of Bank for International development that prefers facts and credible, relevant Settlements, observed that, “The fear of going against evidence, over opinion, intuition, anecdotes and the manifest view of markets can have a powerful common sense to take decisions. inhibiting effect.” The International Monetary While we may not have at our disposal the Fund in discussing regulators’ implementation of rigorous approach of randomised controlled trials, macroprudential policy tools, also notes “biases in the principle is certainly inspired by it. We try to look favour of inaction.” for evidence on what works and what not. This may Roberta Romano of Yale Law School contends sometimes be difficult and challenging because of that there is an Iron Law of Financial Regulation: non-availability of local data. In such cases, we use following financial crises, Congress enacts legislation international standards, which are customised to our that increases financial regulation. She terms them context and conditions. Let me give a few examples of as a regulatory ratchet. You are all aware that the evidence-based regulation making. ratchet effect describes a process or an economic 4 Framework for Formulation of Regulations (May 2025) phenomenon that, once set in motion, is difficult to 4 RBI Bulletin November 2025Regulation by RBI: Some Reflections SPEECH reverse, similar to how a mechanical ratchet moves in We keep this trade-off in mind while formulating one direction only. Similarly, Romano contends that regulations. We attempt to strike the right balance, new statutes are layered atop existing laws and new keeping in view the benefits and costs of each and regulations are grafted onto existing ones, creating an every regulation. This has been embedded in our increasingly complex and opaque regime. regulation making framework, which we formalised in May this year. This calls for a periodic review of regulations. The FSDC under the chairpersonship of the Finance Before releasing draft or final guidelines, we Minister has decided that all financial regulators thus try to estimate direct and indirect impact of our conduct a review of all regulations every 5-7 years. We proposed regulations. Where exact calculations are too have started the process. difficult, we tend to rely on estimates. This helps us finetune the measures to ensure that cost of regulation A number of measures5 that we announced in is not weighing down the benefits disproportionately. October, this year are a result of the review of the regulations. Some of these regulations were framed to The Boundary Problem: What Should Be Regulated? meet the twin balance-sheet problem post the Global A subset of the cost-benefit trade-off is whether Financial Crisis, in the wake of aggressive fiscal and to regulate or not. Financial innovation continuously monetary stimulus. The regulatory and supervisory creates entities and activities at the regulatory frameworks have been strengthened since then. The perimeter. Stablecoins, cryptos, buy-now-pay- performance and health of the banking sector is much later schemes, etc. challenge traditional regulatory improved. As a result, these regulations needed to categories. Should they be banned, tolerated, or be reviewed and accordingly they were reviewed and brought within regulatory frameworks? finetuned. This is a deep question, which needs judgment V. Challenges in Regulation Making and Enforcement about the balance between potential benefits and Having covered the main principles of regulation risks. The rapid pace of innovation means that this making, I will now describe the challenges that we judgement needs to be made continuously so that face in this regard. Creating effective regulation is regulatory approaches evolve and keep pace with the an exercise in navigating multiple tensions. Let me needs of the times. The RBI has not only kept pace highlight five of them. with the development but has been proactive in this Balancing Cost-benefit Trade-offs regard, adopting a balanced and prudent approach. It adopted a cautious approach to cryptocurrencies The first challenge is to balance the cost-benefit contrasts while taking an enabling stance towards trade-off. While regulations do offer benefits in terms regulated digital innovations like UPI or digital of stability, consumer interest, etc, they come with a lending. cost. Economic interest warrants increasing efficiency, and promoting innovation. We recognise that just like The Innovation-Stability Trade-Off there are no free lunches, regulation too is not devoid A related dilemma is how much innovation of costs. There are trade-offs between the benefits and to permit when its risks are not fully understood. efficiency. Excessive caution stifles beneficial innovation 5 Statement on Developmental and Regulatory Policies, RBI (Oct 2025) and can drive activities underground. Excessive RBI Bulletin November 2025 5SPEECH Regulation by RBI: Some Reflections permissiveness can allow risks to accumulate and waivers, while providing short-term relief, delayed even threaten financial stability. recognition of fundamental problems. The RBI’s shift towards tighter enforcement, including the The RBI’s regulatory sandbox approach represents Insolvency and Bankruptcy Code’s prompt resolution an attempt to balance the trade-off - creating controlled provisions, reflects this approach. Yet, the COVID-19 environments where innovations can be tested under pandemic prompted temporary regulatory relief regulatory oversight before being scaled. However, measures, as is the case with trade-related regulatory questions remain about the optimal design of such measures taken recently. These illustrate that context sandboxes, the criteria for graduation, and the balance matters. between experimentation and protection. Our approach towards forbearance is clear: Procyclicality in Regulation forbearance should be exceptional, time-bound, and Fourth, sometimes regulations themselves can transparent. It should not become a substitute for amplify business cycles. Emerging markets face addressing underlying problems. particularly acute procyclicality challenges because VI. Conclusion: Regulation as an Evolving Discipline capital flows amplify domestic cycles. Mark-to- market accounting forces institutions to recognize Let me now conclude. The intent of my detailed losses during downturns, potentially triggering fire treatment of this topic today was to expose you to a sales. Capital requirements, if not designed carefully, relatively diverse set of issues, igniting your interest can force deleveraging precisely when credit is in the area of regulation making. most needed. Provisioning norms that respond to As you advance in your academic and professional current conditions rather than expected losses create journeys, I encourage you to view regulation not as a procyclical dynamics. static set of rules but as an evolving discipline that The Basel III framework attempts to address this responds to changing markets, technologies, and risks. Effective regulation certainly requires technical through countercyclical capital buffers - requiring expertise, but also judgment, humility about what banks to build capital during booms that can be regulators can and cannot achieve, and constant released during stress. The RBI has implemented learning. these provisions, but there are questions about their calibration from time to time. The Reserve Bank of India is trying to continuously adapt. We are vigilant and alert to Regulatory Forbearance vs. Strict Enforcement emerging risks and evolving conditions. We are The fifth challenge every regulator faces is with encouraging innovation while being mindful of regard to implementation when strict enforcement our regulatory objective of safeguarding systemic of standards seems to threaten broader stability. The stability. We are trying to simplify regulations where temptation towards forbearance - temporarily relaxing possible while maintaining necessary safeguards. requirements - is understandable but dangerous. It We are strengthening coordination with other can allow problems to fester, create moral hazard, and regulators while respecting jurisdictional boundaries. undermine regulatory credibility. We are trying to enforce rules consistently while India has grappled with this dilemma. Repeated recognizing that circumstances sometimes warrant restructuring schemes for stressed assets and loan flexibility. 6 RBI Bulletin November 2025Regulation by RBI: Some Reflections SPEECH These tensions cannot be permanently resolved Scientific Committee No. 8, European Systemic Risk - they must be continuously managed. Managing this Board. requires not just regulators but informed citizens, 2010, The Ultimate Quotable Einstein, Edited by Alice responsible financial institutions, engaged scholars, Calaprice. “It can scarcely be denied that the supreme and yes, bright students like yourselves who will shape goal of all theory is to make the irreducible basic the future of Indian financial system. Achieving good elements as simple and as few as possible without regulatory outcomes is almost always a collaborative having to surrender the adequate representation of a effort: by the government, amongst regulators, the single datum of experience.” regulated, and the broader community. Status quo bias in decision making, Samuelson and I wish all of you a great success in all your Zeckhauser 1988 endeavours. Claudio Borio: “Implementing the macroprudential Thank you. Namaskar. Jai Hind. approach to financial regulation and supervision” References: (2009) Stiglitz, J. E. (2009). “Regulation and Failure,” in New Staff Guidance Note on Macroprudential Policy, Perspectives on Regulation, ed. David Moss and John IMF (2014); Macroprudential Policy: An Organizing Cisternino, Cambridge: The Tobin Project. Framework (2011) Diamond, D. W., & Dybvig, P. H. (1983). “Bank runs, Are There Empirical Foundations for the Iron Law deposit insurance, and liquidity,” Journal of Political of Financial Regulation? Roberta Romano Yale Law Economy, 91(3), 401-419. School, NBER and ECGI, Revised February 4, 2024 Haldane, A. G., & Madouros, V. (2012). “The dog and IMF Policy Paper (2022). “Review of The Institutional the frisbee,” Speech at the Federal Reserve Bank of View on The Liberalization and Management of Kansas City’s Economic Policy Symposium, Jackson Capital Flows” Hole, Wyoming. OECD (2014), The Governance of Regulators, OECD Regulatory complexity and the quest for robust Best Practice Principles for Regulatory Policy, OECD regulation (June 2019), Reports of the Advisory Publishing RBI Bulletin November 2025 7The Evolving Facets of Regulations SPEECH The Evolving Facets of At the same time, economic interest warrants increasing efficiency, and promoting innovation, Regulations* which too is our duty. We recognise that just like there are no free lunches, regulation to enhance Shri Sanjay Malhotra stability too is not devoid of costs. There are trade- offs between stability and efficiency. I am delighted to be here amongst all of you in I had assured that we will keep this trade-off in the SBI Banking and Economics Conclave. I sincerely mind while formulating regulations. I had mentioned thank Chairperson, SBI for providing me this that it will be our attempt to strike the right balance, opportunity to address the leaders of the banking and keeping in view the benefits and costs of each and finance in the country. This Conclave is happening at every regulation. The recent regulatory proposals a time when we are navigating a world of continued strive to maintain this balance - the balance between uncertainty, geopolitical realignment, and rapid the drive to innovate and grow and the duty to protect. technological change. This brings not only a host of challenges but also many opportunities. Moreover, in a rapidly evolving banking system, underpinned by technological advancements of I recall that about a year ago, when I addressed this tectonic magnitude, no regulator can afford to situate Conclave in my capacity as Revenue Secretary, I spoke the system at a point in time. The role of the regulator about tax reforms. In my address today, I propose to is to guide its evolution within guardrails that ensure dwell on the recent regulatory measures the RBI has stability, fairness, and resilience. taken. Earlier, we had issued the Regulation making Framework. We follow a consultative process, do an In India, this philosophy has found steady impact analysis and provide the objectives of the expression in RBI’s approach towards regulation, what regulations. In the spirit of this framework, I wish might be called responsive conservatism - a model to shed some more light on the objectives and the that prizes stability yet remains open to reform. rationale of the major regulations. The Backdrop The Purpose of “Regulation” In this context, it would be germane to briefly In this context, I would also like to reiterate recap the developments over the past decade in the what I said in my first MPC statement in February Indian financial system. this year. The interest of the national economy Post GFC, in the wake of aggressive fiscal demands financial stability. For us in the Reserve and monetary stimulus, the ensuing regulatory Bank, financial stability remains the north star, for forbearance sowed the seeds of the “twin balance short term growth achieved at the cost of financial sheet” problem - overleveraged corporates and stability can have bigger consequences for long-term stressed bank balance sheets. Coupled with the growth. Research shows that financial instability may manifestation of external sector vulnerabilities, India not only more than offset the gains of higher short got clubbed among the so-called “Fragile Five.” term growth, but also make recovery more distressful Guided by the principle “never waste a good and longer. crisis”, the period from 2014 led to a foundational * Keynote Address by Shri Sanjay Malhotra, Governor, Reserve Bank surgery to restore the long-term health of the financial of India at the 12th SBI Banking & Economics Conclave - 2025, Mumbai, November 7, system. RBI Bulletin November 2025 9SPEECH The Evolving Facets of Regulations This was driven by a series of regulatory perspective, credit and deposits have expanded to measures aimed at recognition, resolution and almost 3 times1. Capital buffers have strengthened recapitalisation of the banking system. The Asset too - the CRAR rose from 13.5 per cent as on 31st Quality Review (AQR), initiated in 2015, compelled March, 2015 to 17.5 per cent as on 31st March, 2025 banks to recognise the true state of their loan with CET-1 increasing from 10.43 per cent to 14.73 books, bringing hidden NPAs onto balance sheets. per cent during the same period. Asset quality has Significant improvements were also brought about in also improved. GNPA and NNPA have reduced to 2.3 the supervisory framework. per cent and 0.5 per cent in March 2025 after rising to Simultaneously, Prompt Corrective Action highs of 11.2 per cent and 5.96 per cent respectively (PCA) framework helped restore the health of weak in March 2018. Profitability of banks has enhanced banks. It was followed by consolidation of 27 public significantly. Between FYs 2017-18 and 2024-25, sector banks into 12 by 2020. These measures Return on Assets increased from -0.24 per cent to were complemented by a massive recapitalisation 1.37 per cent, and Return on Equity jumped from -2 programme, which strengthened capital buffers and per cent to 14 per cent. Regulation cannot ignore this restored their capacity to resume healthy lending. performance, these changed realities. The introduction of IBC in 2016, and the pursuant This evolution implies that prudential rulebooks resolution paradigm introduced through out of too should evolve in a calibrated manner as banks court workout mechanisms, have fundamentally are now stronger and supervision more alert even transformed India’s credit culture. as alternative risk-bearing pillars have deepened and market-based risk transfer mechanisms have become Parallelly, major reforms were undertaken to more effective. strengthen monetary and macroeconomic stability, including the adoption of a flexible inflation-targeting The recent regulatory measures need to be seen regime, deepening of forex markets, and the gradual in the backdrop of these developments. Let me now liberalisation on the capital account. elaborate on a few measures. The recent past has also seen structural Capital Market Exposure (CME) transformation of financial intermediation into a I will first talk about the draft guidelines on sophisticated and layered system. Nimble FinTechs capital market exposure. The proposals to enhance and NBFCs now assume a greater role in sourcing the limits for lending to individuals against securities and origination. Development of capital markets and and rationalise the norms for lending to capital credit risk transfer channels such as securitisation market intermediaries are part of the normal process now provide a conduit for risk transfers. The Project of review, seeking to reset the limits, set way back Finance Directions issued recently, address risks in 1999. Importantly, the revision in limits has been arising from regulatory approvals and availability of land. The proposed forward-looking ECL provisioning accompanied by a more structured Loan to Value (LTV) will help early recognition of deterioration in asset framework, sensitive to the risks of the underlying quality. securities. Moreover, Indian banks today are far more 1 Source: Compiled from RBI DBIE Returns Bank deposits: 2015 – 85.33 Lakh crore; 2025 – 225.8 Lakh crore mature than they were a decade ago. To put this in Bank credit: 2015 – 65.36 Lakh crore; 2025 – 182.43 Lakh crore 10 RBI Bulletin November 2025The Evolving Facets of Regulations SPEECH The proposed removal of limits on loans against 26 lakh crore rupees in the last ten years. On the debt instruments, while retaining the regulatory limits other hand, overall share of exposure of the banks to for equity instruments, recognises the fundamental corporates has considerably reduced since then. The difference between the two instruments from a risk regulatory landscape, as mentioned earlier, too has perspective. The key risk a debt instrument carries is evolved significantly. The large exposure framework, credit risk, and just like loans, credit risk is expected which is aligned with international best practices, to be managed as part of the broader credit risk is now well-established and the supervisory tool management framework. An additional comforting kit is vastly improved. Therefore, it is proposed to factor is that only listed and investment grade debt substitute the blanket risk weights and provisions securities are proposed to be permitted as collateral. in the extant framework with better monitoring and This rationalisation is also expected to foster a risk management by the banks. virtuous positive feedback loop for the development Reduction in Risk Weights for Infrastructure of the bond market. exposures of NBFCs As regards acquisition finance, it is acknowledged Coming to risk weights for infrastructure as an integral element of an evolved financial system, exposures of NBFCs, I would like to highlight that that helps in better allocation of financial resources. the proposal seeks to prescribe risk weights on the Recognising its need, non-bank players such as NBFCs basis of the risk profile of the exposures. However, and bond markets are already allowed to provide such certain conditions have to be satisfied to qualify for funding. Removal of the restriction on banks will a lower risk weight: one, there is a set of qualitative benefit the real economy. The proposed guardrails criteria that has to be fulfilled ab initio, including a like limiting bank funding to 70 per cent of deal value, suitable covenant to protect the interest of creditors limits on debt to equity ratio, aggregate exposure through the tenor of the exposure; and two, a limits relative to Tier-1 capital, and eligibility criteria principal repayment criteria that would demonstrate will contain concentration and credit risks, thereby a reasonable performance track record before risk ensuring safety while allowing banks and their weights can be lowered. stakeholders to reap benefits of additional business. Revision in ECB norms Market Mechanism for Large Borrowers As regards, the External Commercial Borrowing Let me now turn to the withdrawal of the (ECB) framework, these measures come against the Specified Borrower Framework. backdrop of a strong external sector. India’s current This framework was instituted almost a decade account recorded a surplus of USD 13.5 billion (1.3 ago, in a very different financial environment. This per cent of GDP) in Q4 FY25, followed by a modest is a unique measure, which perhaps no other country deficit of USD 2.4 billion (0.2 per cent of GDP) in Q1 that has implemented the Large Exposure framework FY26. Foreign exchange reserves stand at about USD (LEF), at the bank level, has. At that time, the banking 690–700 billion, sufficient to cover nearly 11 months system was grappling with elevated levels of stress, of merchandise imports. Capital account remains which is no longer the case. The tier 1 capital of the robust. Net inflows to India under foreign investment scheduled commercial banks has increased 3.2 times (FDI and FPI), external commercial borrowings and from about 8 lakh crore rupees in 2016 to more than NRI deposits stood higher at USD 30.4 bn during RBI Bulletin November 2025 11SPEECH The Evolving Facets of Regulations April-July 2025 than USD 26.8 bn in the same period dangerous to take a cold, to sleep, to drink; but … out last year. Our projections show that capital flows will of this nettle, danger, we pluck this flower, safety. So, remain quite strong during the rest of the year as these measures do reflect fresh thinking, but are well. incremental and do not introduce any sea change. The recalibration of the ECB framework is a Regulation as a whole natural step in India’s financial evolution - grounded Moreover, no regulatory measure can be in strong fundamentals, guided by prudence, and understood in isolation. Each measure has to be seen inspired by confidence in the economy’s capacity to in the continuum of regulatory evolution and not in engage with global finance on its own terms. isolation. These proposals must be read against the The removal of all-in-cost ceilings will encourage broader regulatory scaffolding, which mitigates the competitive rates and promote prudent hedging risks. Together, the regulations create a multi-layered behaviour. Expansion of the universe of eligible defence, to keep systemic risk in check. Analysing merely one regulation without understanding the lenders will improve pricing efficiency. complete regulatory landscape risks missing the Linking the borrowing limits to the borrower’s forest for the trees. net worth under automatic route links ECB to the Amendments based on experience strength of the borrower, while enhancing ease of doing business. This limit and the overall soft ceiling Let me also highlight that the higher of total outstanding ECBs at 6.5 per cent of GDP will responsibilities placed on the banks are based on mitigate the risks of excessive external leverage. their past performance. This is on account of the improved governance and prudent decision-making Moreover, I wish to clarify that ECBs are proposed by the banks over the last decade. As highlighted to be permitted only for FDI-compliant real estate earlier, capital buffers are stronger, profitability projects and remain prohibited for speculative real better, and asset quality much improved. Regulation estate activity such as land or property trading. has to evolve keeping in mind the realities of time Epilogue and the performance of the banks. Appropriate and incremental regulations Promote learning and discovery To summarise, all these measures are balanced Moreover, no regulator can, or should, substitute and appropriate, built on the bedrock of a banking for boardroom judgment, especially in a diverse system that has been systematically fortified over country such as ours. Each case, each loan, each the last decade, with financial stability remaining the deposit, each transaction is different, with varying unwavering cornerstone of our policy architecture. risks and opportunities. We need to allow the All the changes are incremental in nature. As regulated entities to take decisions based on merits Shakespeare would say, we are moving wisely and of each case, rather than prescribing a one size fits all slow; they stumble that run fast2. At the same time rule. This will enable regulated entities to experiment when we aspire to become a developed nation, while and innovate, learn and improve. we move with caution, we need to display courage. Regulations with guardrails Again, I am reminded of Shakespeare’s line3: Tis At the same time, wherever we are allowing 2 Romeo & Juliet, Shakespeare 3 Henry IV, Part 1. hitherto prohibited activities, or reducing restrictions, 12 RBI Bulletin November 2025The Evolving Facets of Regulations SPEECH we have provided sufficient guardrails to ensure The Reserve Bank Ombudsman Scheme is also being safety and prudence. further fine-tuned to enhance its efficacy. Regulation and supervision go hand in hand To sustain the regulator’s efforts, it is imperative that regulated entities address customer grievances I may also mention that the role of a regulator is through mechanisms that are fair, transparent, timely, like that of a gardener whose job does not stop with and without undue cost. The regulated entities may providing the “enabling environment” for the growth please assess the quality of customer service through of the plants. The gardener keeps on monitoring the periodic and regular thematic studies. In addition, growth of the plant and prune unwanted growth effective Root Cause analysis of the grievances may to shape a collective orderly beautiful garden. RBI also be conducted to identify systemic issues, process has ample tools - risk weights, provisioning norms, gaps and repetitive grievances and remedial measures countercyclical buffers - to contain emerging taken. risks. History shows our readiness to act, as seen most recently in the macroprudential measures I would request the MD&CEOs and other top of increasing risk weights on consumer credit in executives present here for their full support and November 2023. Needless to say, supervisory actions their personal attention in ensuring that the proposed have always enabled effective backstops to prune changes, when they are made final, are implemented unwanted growth and shape a robust and resilient not only in letter but also in spirit. banking system. Conclusion Finally, let me emphasise that most of these are To conclude, I would like to emphasise that measures are in the form of drafts issued for formal our approach is calibrated: granting banks greater consultation. These have been issued after great commercial leeway for growth, innovation and ease deliberation, informal consultation and thought, of doing business, while ensuring that risks are accompanied with detailed impact assessment, and minimised and financial stability is maintained. while they do reflect the broad direction, they are not We have set ourselves an ambitious goal of final. We will finalise them after taking inputs from becoming an advanced economy by 2047. The all stakeholders. financial sector has a large role to play in it. RBI Consumer grievance redress remains steadfastly committed to this goal. We will ensure that our financial system evolves responsibly Before I conclude, let me also touch upon to support innovation, growth, and long-term the changes introduced with respect to consumer economic resilience. grievances. The Reserve Bank has maintained a persistent focus on enhancing customer service I am sure that the deliberations in this Conclave standards and strengthening grievance redressal will help in taking forward this developmental systems. agenda. I commend SBI for this initiative and wish the Conclave a huge success. In this direction, the Internal Ombudsmen framework in larger REs is proposed to be improved. Thank You. Jai Hind. RBI Bulletin November 2025 13Transformational Technologies and Banking: Key Issues SPEECH Transformational Technologies India’s experience in digitisation shows that countries who harness technology with foresight and and Banking: Key Issues* responsibility will not only adapt to change but shape it. Our uniquely successful model of leveraging Digital Shri T Rabi Sankar Public Infrastructures (DPIs) like Aadhaar or UPI has not only positioned India as a leading example of Shri Setty, Chairman, SBI, Shri Amara, MD, SBI, digitisation, but also it has set an example for other distinguished leaders and members of the financial countries to follow. For transformational change, it is fraternity. It gives me immense pleasure to be a part not enough that technology is ubiquitous, it should of what feels like, and perhaps is, the nerve centre of also be foundational. the Indian financial system. The theme of the Conclave ‘India’s Quest for Lessons from India’s Digital Journey Self-Reliance in a Fragmented World Order’, makes If we look back today, we can see that India’s this event particularly timely and critical. The banking system has passed through two-and-a- comfortable assumptions of the post-Cold War era half decades of innovations in payment technology of globalisation are fading as we are seeing a re- – starting from ATM networking and moving emergence of protectionist tendencies and re-shoring through a gamut of retail and wholesale digital of critical supply chains. Economies and societies are payment instruments like RTGS, NEFT and IMPS struggling to adjust not just to the rapid pace of change to the game-changing UPI and continuing on to of technology, but also as the fundamental nature experimenting with digital currency. The journey has of technology itself is undergoing a paradigm shift. Technology has always been a catalyst for improving been gradual yet, transformational. What are the main efficiency in delivering financial products, but now lessons that we can glean from this experience that it has become the very foundation upon which the has placed India as a leading example of payments future of financial intermediation rests. innovation? Technology and Banks a. The very first thing to note is that virtually all of these initiatives came from the public Today I want to dwell on a theme that reverberates in the current era of disruptions and sector, whether it is the ATM Switch, or fast-paced changes, the role of technology in banking. NEFT/RTGS or UPI or, moving slightly away Every aspect of finance, from payments and credit to from the financial sector, the Aadhaar. Even savings, investments, regulation and supervision, is the initiatives to set up key institutions – already being redefined through technology. IDRBT, NPCI, IFTAS, and more recently, RBIH – were all public sector initiatives. With powerful technologies like artificial intelligence (AI) and quantum computing already b. The second aspect is that all of these initiatives under way, our challenge is how to embrace them with were by way of creating infrastructures, wisdom and purpose, and ensure that technological specifically digital public infrastructures. evolution is secure, inclusive, resilient, and future- They were situated in what can be termed ready. a public goods space; they were priced like * Keynote Address delivered by Shri T Rabi Sankar, Deputy Governor, public goods – minimal charges or free; they Reserve Bank of India at the 12th SBI Banking & Economics Conclave - 2025 in Mumbai on November 7, 2025. were accessible by all, like public goods. RBI Bulletin November 2025 15SPEECH Transformational Technologies and Banking: Key Issues c. Thirdly, these DPIs were made available entities have taken UPI to where it is today, and that as a foundational layer for technology but for them UPI would not have been able to reach firms to create innovation. This gave the the nooks and corners of the country. Acquisition of Indian approach a uniquely public-private customers and their payments data, was enough of cooperation character, an approach that an incentive for these app providers to extend these resulted in the best of both worlds - while the services even in the absence of any revenue. It is public sector focuses on what it does best – also important to appreciate that these FinTechs had create public infrastructure, the private sector certain basic advantages - focuses on where it has clear competitive a. Technology edge – Fintechs are more agile as advantage - innovation. they have no legacy IT systems, enabling them d. Fourthly, open access to DPIs led to a rise of to use technology that is more conducive to new fintech players such as payment scale up, integrate and upgrade. Banks, with aggregators, PPI issuers, third-party app their core banking systems find it difficult to providers, etc., bringing agility, innovation, modernise and upgrade. and particularly scale. DPI has thus b. Data advantage - Fintechs can access wider, contributed to the growth of the fintech larger and more comprehensive data sources sector itself. (for example across multiple banks and e. Finally, there is a general realisation that spending channels). the new fintech players, mainly because c. Cost advantage – With asset light balance they had no legacy systems that tied them sheets, no physical branches and very little down, were far more nimble and innovative due diligence requirements (KYS, AML/CFT than incumbent banks. While this did not etc), these fintechs incur a lot less cost than undermine the role of banks as such, it banks. exposed the Achilles heel of the banking These advantages were large, and it can system – that banks could be vulnerable to be reasonably argued that banks were unfairly strong inertia in adapting to new technology. disadvantaged (higher regulatory burden, frictions of This leads me to the basic theme of my talk – KYC process and AML checks). In a competitive market, the nature of the challenges new technology banks would have recovered their higher costs from poses for banks. the fintechs, but then, adoption of new technology Banks and new Fintechs would probably have suffered. But even without these disadvantages, it would be reasonable to assume that Let me first explain the vulnerability by using the banks just did not foresee the potential in UPI that the context of UPI. UPI is essentially a payment instrument FinTechs did. Part of the explanation lies in the very that transfers funds from one bank account to another nature of banks. (it can also use wallets, but that is a negligible part of the volume, so we will ignore it for this purpose). All Banks are special entities, unlike any other UPI transactions are therefore payment transactions business. They have an important socioeconomic role, made through banks. Yet when we talk of UPI, the first that of creating money. Because of this role, banks entity that comes to mind is not a bank but a non- are licensed and closely regulated and supervised. bank UPI app. It is well recognized that these fintech This arrangement works to the benefit of banks, 16 RBI Bulletin November 2025Transformational Technologies and Banking: Key Issues SPEECH because entry is not free and there is some degree of payment leg. This is something only a bank underwriting by the State. It also has a disadvantage could do. With the blockchain technology, that banks have to bear the cost of regulation, both this could well change. The basic function financially and in terms of the obligation to follow of a blockchain is to authenticate financial prudential processes. One corollary of this somewhat transactions in the absence of a trusted protected environment within which banks operate is intermediary. It is now possible that that their innovation edge is blunted. This is probably banks may not be required to authenticate the reason banks did not fully appreciate the potential payments, substantially impacting their role benefits of UPI, as keenly as the fintech players did. as intermediaries. If this indeed is true, it is time the banking Apart from these fundamental challenges, new system thought hard and deep about the challenges technology poses various other risks to the roles that from the transformational technology changes we are banks traditionally play. For instance, digital currencies living through. Technologies like artificial intelligence, can provide a superior alternative to banks in cross- blockchain, quantum and digital currencies, will shape border payments. Quantum computing, though the next decade of financial transformation. These nascent, could one day revolutionise encryption, technologies pose challenges that are fundamental to risk modelling, and portfolio optimisation. AI can banks. interpret blockchain data; CBDC can embed smart contracts; IoT devices can trigger automated financial a. Most money in modern economies is bank settlements. Together, they signal a shift from a money. Creating money through extending system of intermediated finance to one of intelligent credit is the most basic function of a bank. interconnections. The advent of digital currencies is now The risks emanating from these technological providing an alternative. We can no longer shifts need to be recognized and understood. True, at assume that banks would always remain this stage these risks are more conceptual than actual, because who else would create money, that yet at the very least they can eat into the exclusive is the lifeblood of modern economies. The domain of banks. Banks, therefore need to be prepared risks from private digital currencies to banks well to meet these challenges and maintain their appears existential, yet not well understood central role in monetary transmission and financial or debated globally. Even with CBDCs, which stability. become a necessary bulwark against private digital currencies, banking business is likely While by now banks have a fairly good to change significantly, and these impacts understanding of how to approach technology need to be understood by banks. It is not adoption, I would only reiterate a few aspects that just the responsibility of a central bank, the need to be kept in mind with respect to adopting the issuer. new transformational technologies. b. Banks are the core intermediaries in financial a. Banks have inherent strengths - credibility, markets. Every financial transaction, whether balance sheet depth and customer base. or not it requires other types of intermediaries Technology asymmetry tends to dilute (e.g., brokers or market-makers) would these benefits. The ability to leverage these always require a bank to authenticate the strengths would depend on the agility and RBI Bulletin November 2025 17SPEECH Transformational Technologies and Banking: Key Issues speed with which banks modernize their innovation within banks and creating incentives for systems and reimagine their business learning and skill upgradation from within. Human processes. expertise to innovate, govern, and responsibly deploy technology remains the differentiator in a digital b. The nature of technology change facing banks world. Institutions must cultivate deep digital and is different. Many technology changes are no data skills at all levels, ensuring teams are equipped longer incremental, they are re-architectural. Platform technologies effectively enable to navigate complexity and seize opportunities. nonbanks to come into the banks’ domain. Equally importantly, banks need to treat fintechs Distributed ledgers undermine the traditional as partners in innovation and create a mutually institutional guarantees that banks provided. beneficial or symbiotic strategic partnerships with Therefore, competitiveness may no longer them. The objective should be to benefit from the depend as much on balance sheet strength agility of fintechs without compromising prudential but on data capability and technology discipline. flexibility. Concluding thoughts c. Since banks are structurally vulnerable because of their monolithic IT systems As we reflect on the transformative absorption and high fixed costs arising from branch of technology in finance, one truth is unmistakable network and compliance costs, incremental i.e., while technology is inevitable, its direction is digitisation is unlikely to be enough to keep intentional. The choices banks make today will shape them competitive. not only the architecture of their IT systems but the experience, inclusion, and trust of millions of citizens In this context, what can be the strategic tomorrow. As technology is rewriting the very DNA imperatives for banks to prepare for transformative technologies? Modernising core infrastructure to make of finance, the preparedness of banks will determine it less monolithic and rigid is one such imperative if whether they lead this transformation or are led by banks have to compete with the fintech ecosystem. it. Institutions that adopt technology strategically, Adopting a platform orientation and API based embed strong governance principles, develop human collaboration with fintechs is another. Perhaps the most capital, and collaborate across the ecosystem will not important requirement is reengineering the culture of only navigate change but will shape it. 18 RBI Bulletin November 2025Where Governance Intent is Strong, Regulatory Gaps and Overlaps Fade SPEECH Where Governance Intent is If we keep editing diagrams without analysing the root cause, we treat symptoms and miss the cause. Strong, Regulatory Gaps and In my view, the reason, therefore, is “intent”. When Overlaps Fade* intent is strong and governance is lived in spirit, overlaps simplify and gaps close. When intent is Shri Swaminathan J. weak, the reflex is to add more rules and procedures – multiplying work but losing sight of the real risk. Chair of the event, Shri M Damodaran; Chairman, The real question is how deeply good governance IRDAI, Shri Ajay Seth; Chairman, PFRDA, Shri S is internalised in everyday decisions and board Ramann; WTM, SEBI, Shri Kamlesh Varshney and oversight. other distinguished guests, colleagues, ladies, and With that lens, let me focus on five practices that gentlemen. A very good morning to all of you. I feel matter most: I am pleased to be here today for the 10th i. Boards must own outcomes, not paperwork. edition of the Gatekeepers of Governance Summit, as ii. Independence should be in substance. conferences like these provide an invaluable platform for the stakeholders to articulate and understand each iii. Look through the group, not just the entity. other’s perspectives. I thank the organisers for this iv. Protect and empower the control functions. opportunity. v. Governance gap analysis with real Our theme today is simple to ask but hard to remediation. answer: “Regulatory gaps and overlaps: do they Let me briefly elaborate on each of these five exist?” I am inclined to agree that they do exist and aspects I propose to address this in two parts, viz. from the standpoint of organisations and thereafter as Firstly, boards must own outcomes, not Regulators. paperwork. A diverse and independent board keeps an organisation on track by overseeing compliance, Most organisations often respond to governance risk, culture, and ethics. Directors must exercise their questions by redrawing organisation charts, tweaking duty of care and duty of loyalty, and they must own reporting lines, and updating committee charters. outcomes1. Boards must set risk appetite and outcome These fixes do help, but only to a point. goals, and require independent assurance - risk, Business models, technology, and vendor chains compliance, and internal audit - to test what matters change faster than boxes on a slide. As firms expand, and report findings, root causes, and their closure. digitise, outsource, and integrate, two patterns keep Secondly, independence is not a label; it is the showing up: overlaps - two teams or two regulators ability to challenge. It is a posture backed by time, asking for the same thing; and gaps - a new product, information, and courage. Independent directors partner, or dataset sitting outside or at the perimeter should be able to challenge strategy, controls, of any policy. financials, and risk, and to question the assumptions * Remarks by Shri Swaminathan J, Deputy Governor, Reserve Bank of India, on Friday, November 7, 2025, at the Gatekeepers of Governance 1 Corporate Governance Principles for Banks, BCBS, BIS (https://www.bis. Summit in Mumbai. org/bcbs/publ/d328.pdf) RBI Bulletin November 2025 19SPEECH Where Governance Intent is Strong, Regulatory Gaps and Overlaps Fade behind forecasts. Our anonymous 2024 survey - spotting weaknesses, strengthening compliance, and amongst boards of banks revealed that many boards improving risk management2. prefer consensus, and a meaningful minority of A system-wide perspective directors hesitate to voice dissent. The Chair’s role, Modern business is not tidy. A listed company therefore, is to draw out quieter views and keep can be part of a conglomerate with banks, NBFCs, challenge safe. insurers, brokers, payment firms, tech subsidiaries, Thirdly, in large conglomerates, risk does not overseas arms, and associates. The regulatory map stop at the boundaries of individual entities. Boards is equally rich: Company law and MCA, Securities should see the whole, not just the parts. Two steps regulation and listing rules, Sectoral regulators help. First, ring-fence critical entities so a local problem for banking, insurance, pension, competition law, does not become a group crisis. Second, enforce insolvency, accounting and audit oversight, market strict related-party policies. Such transactions can conduct rules, data and cyber requirements, and be legitimate, but they need transparency, fairness, multiple enforcement agencies. Add international and arm’s-length terms. Sound rationale and good obligations, exchanges, depositories, SROs, and state- documentation are evidence of thought and a tool for level authorities. future learning, not a bureaucratic burden. In such a world, some overlap is inevitable. That Fourthly, the three lines of defence must be is not a bug. Overlaps can also act as layers of safety real. Business lines own risk. Risk management and net, ensuring that if one control misses an issue, compliance provide challenge and guardrails. Internal another may catch it. The real problem, may arise audit tests the system independently. The Heads of from conflicting rules, duplicated compliance, and assurance functions (the Chief Risk Officer, Chief uncoordinated enforcement which is avoidable. At Compliance Officer, and Head of Internal Audit) must the same time, new activities, new technologies, and have access to the board and to any business line that new business models can fall between the cracks. can create material risk. They should have adequate So yes, both gaps and overlaps exist. The task for budgets and full access to information. Decisions on regulators is to work together, minimising harmful their appointment and removal should rest with the overlaps and closing material gaps, without impeding board. Weak lines of defence are to be seen as a board innovation. In that spirit, let me offer three principles, failure, not a staffing glitch. may be aspirational in parts, for optimising overlaps In an anonymous supervisory survey of assurance and gaps. heads, most reported strong board backing, but Firstly, regulators must balance entity and activity- almost half said resources do not match their bank’s based regulation. Regulate the activity wherever it size and complexity. It is essential to give assurance happens, and keep stronger rules for entities that heads independence, stature, and adequate resources. hold public trust. If an app offers investment advice, Otherwise, assurance stays ornamental. the advisory guardrails should apply, even if the provider is not a traditional intermediary. If two Finally, markets move faster than rules activities create the same risk, they should face the and regulations. A periodical governance gap same rules - regardless of the label or the provider. analysis helps organisations see where their policies and frameworks stand against industry best practices 2 William, L. (2006). Governance Gap Analysis. DM Review, 16 (8): 30. 20 RBI Bulletin November 2025Where Governance Intent is Strong, Regulatory Gaps and Overlaps Fade SPEECH The second principle is proportionality. the customer identified correctly, and was consent Regulators should scale requirements to risk and captured? However, calibration matters. Outcomes- complexity. A small, simple firm should not bear based rules work best where supervision and the same burden as a large, interconnected group; enforcement are strong and markets are mature. systemically important players should meet higher Conclusion capital, liquidity, control, and disclosure standards. In conclusion, addressing regulatory gaps and Proportionality keeps oversight credible and optimal. overlaps is a journey of continuous improvement that The third is to strive towards outcome-based demands constant reflection, adaptation, and courage regulation, calibrated to market maturity. Where to challenge the status quo. When intent is strong, feasible, make and apply rules to protect outcomes the gaps bridge, overlaps simplify, and governance - fair customer treatment, resilience, true and fair transcends mere compliance to become our shared financials - rather than locking in processes or conscience. technologies. For instance, whether onboarding is on paper or biometric, the test is the same: was Thank you. Jai Hind. RBI Bulletin November 2025 21Policy Frameworks for Economic Resilience: The case of SPEECH Emerging Markets and India Policy Frameworks for Economic IMF has suggested a few factors that are contributing to this resilience.3 These include improved Resilience: The case of Emerging policy frameworks in EMs; the tariff outcomes being Markets and India* milder than what were anticipated earlier; and very limited retaliation by the partner countries.4 In other Dr. Poonam Gupta words, the policy making frameworks in EMs are to be credited for their own economic resilience, as well It is a pleasure for me to be here at the Business as for the resilience in the global economy. The key Standard BFSI Insight Summit. I would like to thank questions of interest, therefore, are: What has made the organisers for this opportunity. this resilience of EMs possible? Is it here to stay? How well has India done on its policy frameworks and In my brief comments, I will be reflecting on economic resilience? the observed economic and financial resilience of emerging markets (EMs) in general, and of the Indian How has the observed economic and financial economy, in particular. In this context, it may be resilience in EMs been achieved? noted that at the recently concluded Annual Meetings After completing my Master’s in Economics at the of the IMF, two contradictory themes prevailed: the Delhi School of Economics and teaching for two years unprecedented global policy uncertainty; and the at Delhi University, I joined graduate school in 1993. In surprising resilience of the economies.1 my second year, I enrolled in a course on International The global economy has shown remarkable Finance. The year was 1994 and a balance of payments resilience to the shifting trade policies and geopolitical crisis was unfolding in Mexico, which spread to tensions. Global growth has held up better than Argentina and select other Latin American economies, anticipated earlier. Currently, inflation outlooks are with the risk of far-reaching contagion to many other mostly benign across countries (notwithstanding countries and regions. Despite having witnessed the the fact that inflation levels in some advanced 1991 crisis at home, I did not have sufficient exposure economies are somewhat higher than their respective to the literature on such crises at that time. targets). Low inflation has provided the headroom While taking this course, and subsequently while for monetary policies to be eased across jurisdictions. pursuing my own research in this area, I learnt more Banking sectors across countries are mostly resilient.2 systematically about the pitfalls of unsustainable * Address by Dr. Poonam Gupta, Deputy Governor, Reserve Bank macroeconomic frameworks. These frameworks at of India - October 29, 2025 - Delivered at the Business Standard BFSI Insight Summit, Mumbai. Inputs received from Asish Thomas George, GV that time consisted of: a fixed exchange rate regime Nadhanael, and Somnath Sharma, and comments received from Indranil Bhattacharya, Anupam Prakash, Sunil Kumar, Sangita Misra and Satyashiba which often resulted in appreciation and eroded Panigrahi are gratefully acknowledged. competitiveness of the real exchange rate and 1 We are focusing here more on EMs, not on advanced economies or low- income economies which have their own unique economic features, large current account deficits. Premature and rapid potentials and challenges. liberalisation of the capital account and financial 2 IMF and other multilateral agencies have also been pointing to the various risks that loom on the horizon. They refer to buoyant equity sector, resulting in excessive external borrowing, often markets (particularly led by technology stocks) leading to worries that a correction could be in the offing. Central banks of advanced economies are in foreign currency (called the Original Sin)5. Lax fiscal concerned about elevated public debt in their respective economies and worry that there might be a disruptive resolution. The financial landscape 3 World Economic Outlook, October 2025. has undergone significant change over the years, with non-bank financial 4 Adaptability and entrepreneurship of the private sector and supportive intermediaries (NBFIs) now playing a larger role in several markets, financial conditions are the other factors. including the bond and credit markets. The growing size, complexity, and interconnectedness of these lightly regulated NBFIs in the financial 5 Eichengreen, B. J., Hausmann, R., & Panizza, U. (2002). Original sin: the system has raised financial stability concerns. pain, the mystery, and the road to redemption. RBI Bulletin November 2025 23SPEECH Policy Frameworks for Economic Resilience: The case of Emerging Markets and India policy and weak fiscal institutions, which combined foreign exchange reserves to act as a cushion against with ad hoc monetary policy frameworks and limited the adverse impact of external shocks to their balance independence of the central banks resulted in fiscal of payments. In other words, they use their foreign dominance and pronounced electoral-fiscal cycles, exchange reserves to modulate large fluctuations in high inflation, and limited policy credibility.6 the exchange rate, or to meet the demand for foreign exchange emanating from a sudden shock to current It became evident at that time that volatile account or reversal of capital flows. capital flows, often triggered by external forces, could upend fragile equilibriums characterised by In addition, they have strengthened their such macroeconomic frameworks. In fact, many domestic macroeconomic policy frameworks by more countries, which had such frameworks in implementing credible fiscal rules, and have adopted place, experienced balance of payment crises in the a rule-based framework for monetary policy with following years. These included, Thailand, South inflation targeting or other close alternatives. They Korea, Indonesia, Malaysia, and the Philippines have strengthened their banking and financial during the Asian crisis of 1997-98; Brazil and Russia sectors. Alongside, they have significantly enhanced in 1998; and South Africa and Turkey in 2001.7 It also the independence of their central banks. became evident that these currency crises could even As a result of these policy efforts, the world for engulf the banking sector, resulting in "twin crises" the most part has not witnessed any country-specific with far graver implications.8 or even regional Balance of Payments crises (barring a In the ensuing years, extensive discussions, handful of exceptions) during the last two and a half introspections, analyses, and research were decades.9 This resilience has been markedly visible undertaken within EMs as well as at the multilateral during the last five years when, EMs had to face institutions. This culminated in a number of policy multiple shocks in succession, such as the COVID-19 reforms undertaken by countries towards sounder pandemic (2020-2021), Russia-Ukraine war (2022) and macroeconomic management, as they learnt from other geopolitical tensions, surge in inflation resulting each other, and were supported by the multilateral in synchronised monetary tightening by Advanced institutions. Economy central banks (2022-2023), and the ongoing trade policy and tariff shocks (2025). The fact that Since early-mid 2000s, EMs have become more EMs, by and large, are able to tide over these shocks cautious in their approach towards the external sector. with relative macroeconomic stability is a testament They have maintained a flexible exchange rate policy to the success of aforementioned policy efforts. (mostly managed floats rather than free floats). They have reduced their liability dollarisation. They have This is not to say that the EMs do not face slowed and recalibrated the pace and sequencing of policy challenges anymore. They do. But instead of capital account liberalisation. They have built up large macroeconomic stability issues, EMs face greater risks to sustained growth, and meaningful employment 6 I ended up writing two of the three papers in my PhD thesis on Twin generation. Crises--when Balance of Payments and Banking Crises occur simultaneously and feed each other. Their key challenge lies in finding the new 7 These have alternatively been called Currency Crises, Balance of Payments Crises or Sudden Stops. sources of growth. In learning to live in a world in 8 Kaminsky, Graciela, L., and Carmen M. Reinhart. (1999). “The Twin Crises: The Causes of Banking and Balance-of-Payments Problems.” 9 Frontier markets are not similarly insulated, their policy frameworks not American Economic Review 89 (3): 473–500. having been similarly evolved. 24 RBI Bulletin November 2025Policy Frameworks for Economic Resilience: The case of SPEECH Emerging Markets and India which trade as an engine of growth is faltering, and, higher interest burden of debt servicing from policy therefore, domestic sources of growth need to play a tightening. While there is no imminent risk to debt larger role. sustainability in EMs, elevated public debt poses a challenge in financing their developmental goals in The acceleration in economic growth witnessed the wake of rising interest payments to service this across EMs in 2000s was driven by a rapid expansion debt. in global trade. The ratio of world trade to GDP increased from about 41 per cent in 1994 to 61 per Where does India stand on the resilience of its policy cent in 2008.10 Since the global financial crisis of framework? 2008-09, however, the global trade to GDP ratio has India’s policy frameworks have continued to flattened, reducing the avenues for EMs to grow faster evolve and are currently among the global best. by leveraging global demand. Its exchange rate, that was pegged until 1991, is More recently, a new threat to global trade increasingly market driven. Its external account has has emerged from the increased incidence of been managed well. There are inherent strengths in protectionism. Apart from reducing the contribution its diversified balance of payments. On the current of external demand to growth, these developments account, the merchandise trade deficit has been also reduce the impact of potential spillover benefits to balanced by strong services exports and remittances domestic growth through channels such as technology receipts. Oil price is not a dampener that it used to be. transfer. Even as some of the trade relations will be All in all, the current account shows resilience and is rebuilt and others will evolve during the course of eminently in a sustainable zone.11 time, the years of hyper globalisation are unlikely to The capital account too gets a variety of inflows, return anytime soon. including FDI inflows that are traditionally known to Neither is the manufacturing sector turning out be stable; and other equity and debt flows, which are to be a sure way to economic success (the potential traditionally known to be relatively fickle, but have of the manufacturing sector seems to have become held up well. India has slowly but surely liberalised its limited due to the existing large players continuing to capital account, but the external debt as a proportion be market leaders). to GDP has been low and stable. The ratio of external debt to GDP has averaged around 20.5 per Another challenge, especially for those economies cent in the last 10 years (end-March 2015 to end- where demography is still favourable, is that under March 2025); as of end-June 2025, external debt to employment remains high, gender gaps remain wide, GDP ratio was 18.9 per cent. Besides keeping the and a large share of workers remain in less productive liability dollarisation in check, India’s short-term informal jobs. debt level too has remained low. The ratio of Short- For some of the EMs, high public debt is also of term Debt (original maturity) to total debt was 18.1 concern. Many EMs undertook fiscal consolidation per cent as of end-June 2025. after they were hit by the crises in the 1990s. However, India has largely adhered to the path of fiscal after the global financial crisis, public debt has risen consolidation, barring periods of significant shocks steadily, exacerbated further by cascading shocks such as the pandemic. Importantly, the composition such as the fiscal stimulus during the pandemic, and 11 During the last 10 years, the current account deficit (as % of GDP) 10 Source: World Bank. https://data.worldbank.org/indicator/NE.TRD. remained in the range of 0.6 to 2.1 per cent, barring the COVID year (2020- GNFS.ZS 21) where it recorded a surplus of 0.8 per cent. RBI Bulletin November 2025 25SPEECH Policy Frameworks for Economic Resilience: The case of Emerging Markets and India of debt, and the improved quality of public spending rate of 7.8 per cent during Q1:2025-26, various high has rendered public debt safe. Most of the public debt frequency indicators point towards a robust expansion is long term, is denominated in local currency, and in Q2:2025-26 as well. In the latest monetary policy is held domestically (a large part of which is held by statement, growth forecast for FY2025-26 has been institutional investors). Furthermore, a favourable revised upwards to 6.8 per cent. Inflation currently is growth and interest rate differential has made current at an eight-year low of 1.5 per cent. As per the latest level of public debt sustainable.12 assessment of the RBI, CPI inflation is projected to be 2.6 per cent for the full year 2025-26, much below the Flexible Inflation Targeting framework for target. monetary policy, introduced in 2016, was a major structural reform in India. Evidence points towards Concluding thoughts improved outcomes post adoption of flexible inflation Having learnt from the crisis decade of the targeting: inflation has become lower and less volatile; 1990s, EMs have put in place policy frameworks and inflationary expectations are better anchored; and decision-making processes that have made them less the transmission of monetary policy has become vulnerable to macroeconomic and financial instability. more effective. Inflation targeting has brought in India has been a frontrunner in implementing these greater transparency to policy making. Frequent reforms. As a result, while the intensity of external communication has helped in anchoring expectations shocks may not have declined, the variability of and in building credibility. There is continuous the macroeconomic outcomes has moderated engagement with stakeholders, making monetary considerably. This economic resilience has enabled policy a two-way consultative process. the policy makers to focus on reforms to enhance As a result of the full matrix of policy reforms, productivity, facilitate ease of doing business, and India’s GDP and per capita income growth rates have improve the quality of financial intermediation. Such accelerated over time; growth has been among the collective efforts are surely putting India on the path highest globally; and its variability has declined.13 to graduate from an emerging to an emerged market India’s near-term growth outlook is promising status in the coming decades (and possibly even in the too. After growing at a stronger than anticipated coming years). 12 Another crucial factor is that, of the total debt of the government of India, external debt consists less than 5 per cent, which mitigates the external sector risks (Receipts Budget 2025-26, Government of India). 13 Indian economy grew by an average of 7.8 per cent during the last three years (2022-23 to 2024-25) making it the fastest growing major economy. CPI inflation declined from a peak of 6.7 per cent in 2022-23 to 4.6 per cent in 2024-25. As per the latest available data, CPI inflation was at 1.5 per cent in September 2025. 26 RBI Bulletin November 2025Central Bank Accounting Practices: The Reserve Bank of India and SPEECH Global Approaches Central Bank Accounting Unique Role of Central Banks Practices: The Reserve Bank of Central banks are unique in two ways. First, they are public policy institutions that operate without India and Global Approaches* any profit motive. Consequently, their balance sheets reflect the policy measures they undertake to address Shri Shirish Chandra Murmu the prevailing economic conditions of the country during a given period. Second, since a central bank Distinguished guests and my colleagues, Namaste possesses the exclusive authority to create money, and a very good morning! it cannot go bankrupt in the usual sense. In other It gives me immense pleasure to address this words, even if its balance sheet shows losses or august gathering of distinguished central bankers negative equity, it can still carry out its functions. from diverse regions, expert speakers associated Central bank mandates vary widely across with renowned international institutions and jurisdictions, reflecting their historical and my fellow colleagues from Reserve Bank of India institutional contexts. Despite the differences in (RBI) at this first International Conference on mandates, functions or roles across countries, at the Central Bank Accounting Practices organised by RBI heart of every central bank is monetary policy and jointly with the SEACEN Centre. I am glad that the financial stability. Central banks aim to maintain topic of Accounting in Central Banks has attracted adequate capital and reserves/ risk buffers to be interest amongst central bankers across the globe able to perform these critical functions effectively. and more than 20 countries are participating in this The Reserve Bank of India has one of the broadest event. mandates, functioning as a full-service1 central bank The purpose of this conference is to collaborate that undertakes a wide range of responsibilities and understand the diverse accounting practices typically associated with a central bank. across central banks, learn from each other, When it comes to central bank capitalisation, I deliberate on certain globally accepted best practices, believe adequate capitalisation is absolutely crucial, and improve the transparency and consistency in particularly for central banks of emerging and accounting practices. In my remarks today, I would developing economies. These central banks not only briefly speak about the unique role played by central pursue domestic monetary stability but also play a banks, importance of their balance sheet and certain vital role in managing external sector stability amid accounting practices that influence central banks’ volatile capital flows and the spill-over effects of financial statements while sharing RBI’s approach monetary policy shifts in advanced economies. A to these aspects. I would also touch upon some well-capitalised central bank elevates a country’s emerging areas of discussion in central bank standing and supports the resilience of the financial accounting. sector. * Keynote Address delivered by Shri Shirish Chandra Murmu, 1 Monetary policy formulation, currency management, regulation and Deputy Governor, Reserve Bank of India on November 14, 2025, at supervision of the financial system, payment and settlement systems, first International Conference on Central Bank Accounting Practices reserves management, banker to banks and the governments, debt organised by Reserve Bank of India jointly with the SEACEN Centre in manager of the governments, foreign exchange management, regulation Mumbai. Inputs provided by Sangeeta Lalwani, Vyom Gupta and Akshay and oversight of key segments of financial markets such as money markets, Vartak are gratefully acknowledged. g-sec market and forex markets, developmental functions etc. RBI Bulletin November 2025 27SPEECH Central Bank Accounting Practices: The Reserve Bank of India and Global Approaches In the absence of any internationally recognised remains vested with Government of India. The way risk capital framework for central banks, each central RBI prepares its financial statements and sets its bank finds its own balance between the opportunity accounting policies is guided mainly by the RBI Act of cost of central bank capital vis-à-vis the socio- 1934 and the RBI General Regulations of 1949. Over economic cost and the negative consequences of time, within this legal framework, these policies have under-capitalisation. evolved to keep up with changing needs and practices. Accounting Standards for Central Banks I am pleased to say that the Reserve Bank of As widely understood, there is no single globally India has a strong and resilient balance sheet, with accepted accounting standard designed specifically adequate level of risk provisioning. Over the years, for central banks and hence, their accounting and RBI has consistently worked to align its accounting disclosure practices vary considerably in format, practices with global best practices, while staying true depth, and emphasis. While some central banks to core principles of prudence and conservatism. have adopted the principles set out in International I would like to highlight a few key aspects of RBI’s Financial Reporting Standards (IFRS), either in full or accounting policy across five crucial areas: – a) Legal with modifications to suit their specific needs, others Framework, b) Prudence in Accounting, c) Surplus continue to apply their own national accounting Distribution Policy, d) Strength of Balance Sheet, and standards or use hybrid frameworks tailored e) Disclosures. specifically for the central bank. Legal Framework The accounting policies chosen by central banks play a crucial role in shaping their balance The Reserve Bank of India Act of 19342 lays down sheets. Major areas of accounting policy that have two key principles that define how RBI operates a significant impact on the capital position and from an accounting standpoint. First, it mandates income recognition frameworks of central banks that the issuance of banknotes be handled by a include, (i) Revaluation frequency of investments distinct Issue Department, entirely separate from the (ii) Treatment of unrealised revaluation gains/ losses Banking Department, with its assets used solely to (iii) Provisioning methodology/ maintenance of risk meet its own liabilities. In other words, the assets buffers and (iv) Surplus distribution policy. A review and liabilities of the Issue Department are kept of publicly available information indicates that central entirely separate from those of the Bank’s other banks represented at this conference follow a wide operations. Secondly, the Act specifies how the spectrum of approaches across these key accounting Bank’s surplus is to be managed. Once provisions dimensions. These variations reflect differences in have been made for bad and doubtful debts, statutory mandates, institutional objectives, risk depreciation, employee benefits, and other standard management philosophies, and the broader economic banking requirements, any remaining surplus must context within which each central bank operates. be transferred to the Government. Together, these Accounting Practices Followed by RBI provisions lay the foundation for how the RBI manages its balance sheet and upholds transparency Let me now briefly talk about the accounting in its financial operations. practices followed by the Reserve Bank of India. Just to give a context, the entire ownership of RBI 2 Section 33, 34 and 47 of the Reserve Bank of India Act, 1934. 28 RBI Bulletin November 2025Central Bank Accounting Practices: The Reserve Bank of India and SPEECH Global Approaches Prudence in Accounting and losses are recorded in the income statement, reflecting their impact on financial performance. This Prudence in accounting reflects in revaluation conference would be a good forum to understand of the assets at fair/market value, conservatism in diverse perspectives, rationale, and methodology for treatment of unrealised gains/ losses and a consistent these classifications from fellow central bankers. application for recognition of the realised exchange gains/ losses. Over the years, RBI has built provisions Surplus Distribution Policy as Contingency Fund (CF) and Asset Development RBI has a transparent, publicly disclosed and rule- Fund (ADF) from realised profits. The Revaluation based surplus distribution policy under the Economic Accounts viz., Investment Revaluation Accounts, Capital Framework (ECF). This framework, introduced and Currency and Gold Revaluation Account (CGRA), in 2018-19, is based on recommendations of an reflect the unrealised gains/ losses from revaluation independent Expert Committee4. The ECF recognises of investments and translation of foreign currency that realised equity should cover the monetary and assets to Indian Rupee. financial stability risks, credit, and operational risks RBI revalues the entire forex reserves portfolio while the revaluation balances should cover the on a daily basis and does not carve out any portion market risk. After making the required provisions, for amortised valuation. All foreign currency assets the remaining surplus is transferred to Government. and Gold are translated to Indian Rupee daily at Since the introduction of the Economic Capital market exchange rates prevailing on the day, which Framework, Reserve Bank of India has consistently gets reflected under the CGRA. Domestic securities maintained its risk buffers at the prescribed levels, are mark-to-market on a weekly basis and also at end even in the face of unprecedented challenges such of each month. as the Covid-19 pandemic and the subsequent global monetary tightening. As we strive for continuous As a prudent accounting practice, RBI does not improvement and refinement, the ECF was recently recognise unrealised revaluation and translation reviewed internally5, and risk assessment has been gains on securities and gold as income but reflects made more granular. them as revaluation balances on the balance sheet. On the other hand, any unrealised losses on Strength of the Balance Sheet revaluation of domestic/foreign securities are charged The prudent accounting policies over the years to the Contingency Fund at the end of the year when have ensured that RBI has a strong and resilient accounts are finalised. There is no fungibility between balance sheet with risk provisions in form of Realised the various heads under revaluation, implying RBI Equity and Revaluation Balances, currently at 7.5% prudently provides for any revaluation loss on and 17.4% of the balance sheet, respectively. Hence, account of investments and does not offset it with a with an economic capital of about 25% of balance positive CGRA balance and vice-versa. sheet, RBI is in a formidable position to effectively International practices3 on these aspects are quite fulfill its public policy mandates while ensuring interesting. Some central banks revalue a portion of monetary and financial stability. their portfolio at fair value, while keeping the rest at 4 Report of the Expert Committee to Review the Extant Economic Capital amortised cost. In some countries, unrealised gains Framework of the RBI, August 2019. 5 Economic Capital Framework of the RBI – Internal Review of the 3 As observed from published annual reports of various central banks. Framework, May 2025. RBI Bulletin November 2025 29SPEECH Central Bank Accounting Practices: The Reserve Bank of India and Global Approaches Disclosures research and discussions. Some research papers6 have tried to explore how the design choices for CBDCs The central bank disclosures need to strike a fine adopted by central banks may shape people’s balance between transparency and confidentiality. behaviour with respect to adoption of CBDC and They need to be transparent enough to effectively potential substitution of banknotes and/or bank communicate the central bank policy operations deposits with CBDC. It is also being discussed and and their financial implications, while maintaining debated globally whether and how this may impact reasonable confidentiality of market sensitive central bank balance sheet structures and the need information. RBI provides comprehensive and for liquidity operations. detailed information for each accounting head, along with significant accounting policies, in its These emerging aspects would require ongoing Annual Report. Additionally, a weekly snapshot engagement and collaboration in future as central of RBI’s balance sheet, foreign exchange reserves, banks learn from their respective experiences. We liquidity operations, and variations in reserve money should work closely in these areas and share our components and sources is also published. This experiences and research with each other, which will regular flow of information ensures transparency help all of us in making better decisions. in communication about our policy actions and the Conclusion evolving trends in the economy. As I end my address, I must say that this Emerging Areas in Central Bank Accounting and conference is being organised at a very opportune Disclosures time as central banks worldwide are navigating Before concluding, I would like to highlight a the VUCA world (Volatile, Uncertain, Complex and few emerging areas in central bank accounting and Ambiguous). The diverse and wide-ranging policy disclosures which are likely to gain more prominence actions during the pandemic coupled with differing in coming days. Let me begin with the recent sharp accounting practices have resulted in variations rise in gold prices which has garnered a lot of in reported incomes and balance sheets of central attention and discussions globally with respect to banks. Central bank disclosures have assumed an its impact on the central bank balance sheets. RBI even more important role in being able to effectively conservatively revalues the gold holdings at 90% of communicate to the larger public the rationale of the the London Bullion Market Association (LBMA) gold policy actions and the accounting policies adopted. price. However, gold revaluation practices vary across In such an environment, prudence and transparency countries and the impact of high movement in gold in accounting are not just buzzwords but the pillars prices on central bank balance sheets and income which central banks must safeguard. needs wider discussion. The diversity in the accounting practices across The issue of potential impact of Central Bank jurisdictions presents enough scope for dialogue Digital Currency (CBDC) on central bank balance and knowledge sharing amongst the central banks sheets has also been attracting lot of international on certain common accounting principles/practices which are prudent and can further enhance 6 IMF Working Paper: Central Bank Digital Currencies and Financial Stability: Balance Sheet Analysis and Policy Choices, October 11, 2024; transparency. This may facilitate better disclosures ECB Occasional Paper Series: The impact of central bank digital currency on central bank profitability, risk-taking and capital, November 14, 2024. across central banks within the legal framework of 30 RBI Bulletin November 2025Central Bank Accounting Practices: The Reserve Bank of India and SPEECH Global Approaches the respective countries. I am confident that this and deeper cooperation among central banks in the conference marks the beginning of a constructive and years ahead. collaborative journey towards achieving prudent and Wish you all successful deliberations and fruitful consistent central bank accounting practices. Let this outcomes during the conference. be the first step in fostering continued engagement Thank you. RBI Bulletin November 2025 31ARTICLES State of the Economy ‘Making the Horizons Meet’: A Heterodox Approach for Short-Term Inflation Forecasting Multivariate Core Trend Inflation: A New Measure of Core Inflation Nowcasting GDP in India: A New Approach Seasonality in Key Economic Indicators of IndiaState of the Economy ARTICLE State of the Economy* The global PMI composite indicated an expansion in business activity, supported by strong growth in services and resilient manufacturing. Reflecting the Global uncertainty remains elevated, although stifling effect of trade policy uncertainty, most of the October witnessed a slight pullback after more than a major economies continued to witness a contraction year of continuous increase. Concerns persist about the in new export orders in October. heightened exuberance in global equity markets, raising Global commodity prices remained subdued on questions about its sustainability and its implications lower food and crude oil prices. Prices of industrial for financial stability. The Indian economy showed metals rose on fears of a supply shortage and higher signs of a further pick up in momentum, despite imports by China. Gold prices saw a correction from continuing global headwinds. Available high-frequency a record high in mid-October in the second half due indicators for October suggest a robust expansion in to reduced safe haven buying and a stronger dollar. both manufacturing and services activities, supported by festive season demand and the ongoing positive impact of Headline inflation eased in October across most the GST reforms. Inflation has moderated to a historic advanced economies (AEs) and emerging market low and remained well below the target rate. Financial and developing economies (EMDEs). However, it conditions remained benign, and the flow of financial remains elevated in AEs amidst persistent services resources to the commercial sector increased significantly inflation. Central banks, in November so far, by and from a year ago. large maintained status quo on policy rates, awaiting further clarity on the evolving macroeconomic Introduction situation. Global uncertainty remains elevated, although The Indian economy showed signs of a further October witnessed a slight pullback after more pick up in momentum, despite lingering external than a year of continuous increase. World trade sector headwinds. Demand conditions exhibited and policy uncertainties also retreated. Financial signs of improvement with the revival of urban market volatility, which had moderated in October, demand and continued strength in rural demand. resurged in November due to concerns over High-frequency indicators of overall economic stretched valuations in AI stocks. In this context, activity remained robust in October, supported by concerns persist about the heightened exuberance Goods and Services Tax (GST) rate reductions and a in global equity markets, raising questions about its pick up in festive spending. GST collections improved sustainability and the financial stability implications over the previous month, indicating a strong pick of any sharp correction. up in consumer demand. Sowing of all rabi crops is progressing well following the harvesting of * This article has been prepared by Rekha Misra, Asish Thomas George, kharif crops. High-frequency indicators for October Shashi Kant, Rajni Dahiya, Shreya Kansal, Durga G, Yamini Jhamb, Bajrangi Lal Gupta, Gautam, Harshita Yadav, Debapriya Saha, Alice suggest further broadening of manufacturing activity Sebastian, Rashika Arora, Radhika Singh, Sritama Ray, Pratibha Kedia, and continued robust expansion in the services Ashish Santosh Khobragade, Aloke Kumar Ghosh, Aman Tiwari, Sukti Khandekar, Shreya Gupta, Avnish Kumar, Sai Dheeraj Vayugundla sector. Chenchu, Ajay Kumar, Yuvraj Kashyap, Nishant Singh and Rasmi Ranjan Behera. The guidance and comments provided by Dr. Poonam Gupta, Merchandise trade deficit widened to an all-time Deputy Governor, are gratefully acknowledged. Peer review by Rajeev Jain, Suraj S and Abhishek Ranjan is also acknowledged. Views expressed high in October 2025. While exports contracted after in this article are those of the authors and do not represent the views of the Reserve Bank of India. remaining in expansion for three months, reflecting RBI Bulletin November 2025 33ARTICLE State of the Economy the adverse impact from global headwinds, imports year ago. Non-bank sources, primarily corporate bond surged on account of higher gold and silver imports, issuances, credit by non-banking financial companies catering to the festive demand. To mitigate the impact and foreign direct investment to India, were the key of trade disruptions on exports arising on account of drivers, even as bank credit growth remained steady. global headwinds, the Reserve Bank implemented Indian equity markets gained in October- various trade relief measures for exporters with November amidst positive cues on India-US trade immediate effect.1 Tariff exemptions on some deal and healthy corporate earnings for Q2:2025- agricultural products by the US on November 14, 26. Primary market mobilisation also recorded a 2025 will help Indian exports.2 significant increase in October over the previous Headline consumer price index (CPI) inflation month. The initial public offerings (IPOs) mobilisation declined by 1.2 percentage points in October to touch during April-October 2025 was markedly higher than an all-time low in the current (2012=100 base year) last year, with strong participation from both FPIs series. The fall in inflation was driven by a decline and DIIs. in food prices and the GST rate cut on goods and In the midst of continuing uncertainty services prices, besides favourable base effects. The on global trade policies and concerns about their deepening of deflation in CPI food in October took it domestic impact, Indian economy continues to be to its lowest point in the current series. Core inflation resilient to external sector shocks, backed by strong (CPI excluding food and fuel) moderated marginally. services exports, robust remittance receipts, and However, excluding the impact of high gold and silver benign oil prices. Foreign exchange reserves remain prices, the decline in core inflation was steeper, adequate to cushion adverse external shocks. External falling to an all-time low. debt as a proportion of GDP remains low and stable. Financial conditions remained benign with Further, the share of short-term debt in total external system liquidity largely in surplus during the second debt remains low. half of October and November. The weighted average Set against this backdrop, the remainder of the call rate – the operating target of monetary policy – article is structured into four sections. Section II was aligned with the policy repo rate. Yields on three- covers the rapidly evolving developments in the month commercial papers averaged around the same global economy. Section III provides an assessment level. At the same time, interest rates on certificates of domestic macroeconomic conditions. Section IV of deposit edged up slightly while that on treasury encapsulates financial conditions in India, while bills moderated. In the fixed income segment, the Section V presents the concluding observations. yield curve shifted slightly upwards especially at II. Global Setting the longer end. During April-October 2025-26, the flow of financial resources to the commercial sector Global uncertainty remained elevated in October, although there was a slight pullback, increased significantly compared to the same period a a decline for the first time in over a year. Both 1 https://www.rbi.org.in/Scripts/BS_PressReleaseDisplay.aspx?prid= world trade and policy uncertainties retreated. 61626 2 India’s exports of the exempted commodities at US$ 446.0 million Financial market volatility, which had moderated accounted for about 7.6 per cent of India’s total agricultural exports to the US in 2024-25. in October, resurged in November due to concerns 34 RBI Bulletin November 2025State of the Economy ARTICLE Chart II.1: Easing World and Economic Policy Uncertainty a. Uncertainty Indices b. Volatility Indices (Index(Jan=2024), left scale; Index (Jan 2025=100) Index (Jan=2024), right scale) 800 12000 700 10000 600 8000 500 400 6000 300 4000 200 2000 100 0 0 World Uncertainty Index World Policy Uncertainty Index US VIX Emerging Markets VIX World Trade Uncertainty Index (RHS) EURO STOXX VIX Sources: World Uncertainty Index database, and Bloomberg. over stretched valuations in AI stocks (Charts II.1a Business activity, as indicated by PMI indices, and II.1b). expanded at a faster pace across major AEs, including the US, the UK, Japan, and Eurozone, whereas The global PMI composite for October indicated it continued to contract in France. Among major an expansion in business activity, supported by strong EMDEs, business activity expanded in India, China growth in services and resilient manufacturing. New and Russia while it contracted in Brazil (Chart II.2a). export orders for both services and manufacturing Major economies continued to witness a contraction contracted signalling continuing weakness in global in new export orders in October, but India and Russia demand (Table II.1). recorded an expansion (Chart II.2b). Table II.1: Global PMI Composite Accelerated Further, but Export Orders Weakened Oct-24 Nov-24 Dec-24 Jan-25 Feb-25 Mar-25 Apr-25 May-25 Jun-25 Jul-25 Aug-25 Sep-25 Oct-25 PMI composite 52.3 52.4 52.6 51.8 51.5 52.1 50.8 51.2 51.7 52.5 52.9 52.5 52.9 PMI manufacturing 49.4 50.1 49.6 50.1 50.6 50.3 49.8 49.5 50.4 49.7 50.9 50.7 50.8 PMI services 53.1 53.1 53.8 52.2 51.5 52.7 50.8 52 51.8 53.5 53.3 52.9 53.4 PMI export orders 48.9 49.3 48.7 49.6 49.7 50.1 47.5 48.0 49.1 48.5 48.9 49.7 48.5 PMI export orders: 48.3 48.6 48.2 49.4 49.6 50.1 47.3 48.0 49.2 48.2 48.7 49.5 48.3 manufacturing PMI export orders: 50.7 51.3 50.3 50.2 50.2 50.1 48.2 47.9 48.7 49.4 49.3 50.1 49.3 services 50 <<<<<<Contraction---------------------------------------------------------------Expansion>>>>>> Notes: 1. The Purchasing Managers’ Index (PMI), a diffusion index, captures the change in each variable compared to the prior month, noting whether each has risen/improved, fallen/deteriorated or remained unchanged. A PMI value >50 denotes expansion; <50 denotes contraction; and =50 denotes ‘no change’. 2. Heat map is applied on data from April 2023 till October 2025. The map is colour coded–red denotes the lowest value, yellow denotes 50 (or the no change value), and green denotes the highest value in each of the PMI series. Source: S&P Global. RBI Bulletin November 2025 35 42-naJ 42-rpA 42-luJ 42-tcO 52-naJ 52-rpA 52-luJ 52-tcO 310 280 250 220 190 160 130 100 70 52-naJ-10 52-naJ-91 52-beF-60 52-beF-42 52-raM-41 52-rpA-10 52-rpA-91 52-yaM-70 52-yaM-52 52-nuJ-21 52-nuJ-03 52-luJ-81 52-guA-50 52-guA-32 52-peS-01 52-peS-82 52-tcO-61 52-voN-30 52-voN-12ARTICLE State of the Economy Global commodity prices, barring gold and registering a decline (Chart II.3a). Crude oil prices edged silver, remained subdued. In October, the World up in end-October following US sanctions on Russian Bank Commodity Price Index softened on lower oil firms. Thereafter, prices stabilised in November food and crude oil prices. The Food and Agriculture on sluggish demand and the forecast of excess supply Organization’s benchmark for world food commodity conditions3. Copper prices edged up on fears of a prices eased, marking its second consecutive monthly supply shortage in major producing countries. While decline, with food prices barring vegetable oils gold prices strengthened in the first half of October Chart II.3: Commodity and Food Prices a. Commodity and Food Indices b. Gold - Copper - Brent Crude Oil Index (Jan 2024=100) Index (Jan 2025=100) 110 105 100 95 90 Food and Agriculture Organization Food Price Index Bloomberg commodity index World Bank Commodity Price Index Gold Copper Brent Crude oil Sources: Food and Agriculture Organization; Bloomberg; and World Bank. 3 International Energy Agency, Oil Market Report. 36 RBI Bulletin November 2025 42-naJ 42-beF 42-raM 42-rpA 42-yaM 42-nuJ 42-luJ 42-guA 42-peS 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS 52-tcO 160 150 140 130 120 110 100 90 80 70 52-naJ-10 52-naJ-91 52-beF-60 52-beF-42 52-raM-41 52-rpA-10 52-rpA-91 52-yaM-70 52-yaM-52 52-nuJ-21 52-nuJ-03 52-luJ-81 52-guA-50 52-guA-32 52-peS-01 52-peS-82 52-tcO-61 52-voN-30 52-voN-12 Chart II.2: Purchasing Managers’ Index: Comparison across Jurisdictions a. S&P Global Composite PMI b. PMI Export Orders (Index) (Index) 64 56 60 52 56 52 48 48 44 44 40 40 Sep-25 Oct-25 Sep-25 Oct-25 Note: A level of 50 indicates no change in activity, while a reading above 50 signals expansion and below 50 suggests contraction. Source: S&P Global. aidnI eropagniS niapS SU ynamreG ylatI labolG enozoruE KU ailartsuA anihC napaJ adanaC aissuR lizarB ecnarF aidnI aissuR ailartsuA ynamreG ylatI enozoruE SU niapS dlroW ecnarF napaJ anihC adanaCState of the Economy ARTICLE on safe-haven demand, it showed some decline since rally, and the Federal Reserve’s rate cut boosted the latter half, in the wake of softening in safe-haven market exuberance, lifting valuations up. European demand flowing from the US China trade truce and a equities also strengthened on the back of strong strengthening US dollar. However, gold prices exhibited corporate earnings. Japan’s equity market reached bi-directional movements in November on varying record highs as investors anticipated expansionary expectations of a Fed rate cut in December (Charts II.3a fiscal and monetary policies under the newly elected and II.3b). prime minister. China’s markets also gained as easing trade tensions improved sentiment. In November, Headline inflation eased in October across concerns on stretched valuations have triggered some most AEs and EMDEs. However, it remains elevated correction in equity markets (Chart II.5a). in AEs amidst persistent services inflation. In the Euro area, headline inflation eased slightly in In bond markets, US Treasury yields declined October and continued to hover around the ECB’s until the third week of October on safe-haven demand, target. Inflation in the UK eased for the first time a prolonged government shutdown, and Fed rate cut in five months (Chart II.4a). Among major EMDEs, expectations. Yields, however, edged higher from the inflation in Brazil declined to its lowest level since end of October on Fed Chair’s comments tempering January while it continued to moderate in Russia. further rate cut expectations. The JP Morgan Emerging Deflationary pressures in China eased with inflation Markets Bond Index (EMBI) spread narrowed in turning positive reversing a two-month decline October, reflecting reduced risk-off pressures (Chart II.4b). and lower risk premia amid improving emerging market fundamentals and easing trade tensions Equity markets in major economies rallied in (Chart II.5b). October. In the US, renewed investor confidence stemming from a temporary let-up in trade tension The US dollar broadly strengthened till early with China, strong corporate earnings, the AI-driven November on increased safe-haven demand amidst Chart II.4: Inflation Remains Elevated in AEs and Divergent across Economies a. Select AEs b. Select EMDEs (Per cent) (Per cent) 4.0 3.6 3.5 3.0 3.0 3.0 2.5 2.1 2.0 1.5 1.0 Brazil Russia China US UK Euro area Japan South Africa India Source: Bloomberg. RBI Bulletin November 2025 37 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS 52-tcO 9 7.7 7 5 4.7 3.6 3 1 0.3 0.2 -1 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS 52-tcO Chart II.4: Inflation Remains Elevated in AEs and Divergent across Economies a. Select AEs b. Select EMDEs (Per cent) (Per cent) 4.0 3.6 3.5 3.0 3.0 3.0 2.5 2.1 2.0 1.5 1.0 Brazil Russia China US UK Euro area Japan South Africa India Source: Bloomberg. 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS 52-tcO 9 7.7 7 5 4.7 3.6 3 1 0.3 0.2 -1 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS 52-tcOARTICLE State of the Economy the US government shutdown, and lower expectations in October, conditional on the evolving domestic of Fed rate cut in the December meeting. However, growth inflation balance.4 In November so far, most it fell thereafter as markets awaited official data of the central banks surveyed held their key policy releases post US government reopening. Emerging rates. Among AEs, while the US, Canada, and New Zealand reduced benchmark rates in October due market currencies remained volatile, in tune with to concerns about a weakening labour market, the developments in US-China trade negotiations European Central Bank, the Bank of Japan, and and varying expectations regarding Fed monetary the Bank of Korea held benchmark interest rates policy (Chart II.5c). Even as debt flows moderated, steady, adopting a cautious, data-driven approach portfolio flows to major emerging markets improved amidst ongoing external challenges. Amongst the in October driven by a surge in equity flows on EMDEs, Philippines, Saudi Arabia and Russia cut strong macroeconomic fundamentals and elevated their policy rates citing a combination of inflation expectations of US rate cut (Chart II.5d). and deteriorating growth outlook. China, Thailand Central bank monetary policy rate actions and Indonesia held the benchmark rates steady, across AEs and EMDEs presented a mixed picture adopting a cautious approach to monitor the impact 350 4.7 330 4.5 310 4.3 4.1 290 270 4.1 250 246.9 3.9 230 4 Out of the 21 central banks monitored, 18 held the monetary policy meetings in October and November so far. Roughly 60 per cent held the rates unchanged, while others opted for rate cuts, assessing the latest domestic growth and inflation figures. 38 RBI Bulletin November 2025 52-naJ-10 52-naJ-91 52-beF-60 52-beF-42 52-raM-41 52-rpA-10 52-rpA-91 52-yaM-70 52-yaM-52 52-nuJ-21 52-nuJ-03 52-luJ-81 52-guA-50 52-guA-32 52-peS-01 52-peS-82 52-tcO-61 52-voN-30 52-voN-12 Chart II.5: Global Financial Markets a. Equity Indices: Select Economies b. Government Bond Yields Index (April 07, 2025=100) (Per cent, left scale; Index, right scale) S&P 500 SSE Composite Index Nikkei 225 STOXX 600 Note: Equity markets are represented by S&P 500 for US, SSE Composite Index for US Govt Bonds JPMorgan EMBI Global Spread (RHS) China, Nikkei 225 for Japan and STOXX 600 for Europe. Source: Bloomberg. Source: Bloomberg. c. Currency Indices d. Portfolio Flows to Emerging Markets (Index, left scale; Index, right scale) (US$ billion) 50 30 10 -10 -30 -50 MSCI EME currency index Dollar index (RHS) Debt Equity Total Source: Bloomberg. Source: Institute of International Finance. 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS 52-tcO 165 155 145 135 125 115 105 95 52-rpA-10 52-rpA-91 52-yaM-70 52-yaM-52 52-nuJ-21 52-nuJ-03 52-luJ-81 52-guA-50 52-guA-32 52-peS-01 52-peS-82 52-tcO-61 52-voN-30 52-voN-12 1,860 110 1,840 108 1,820 1825.8 106 1,800 104 1,780 102 1,760 100.2 1,740 100 1,720 98 1,700 96 52-naJ-10 52-naJ-91 52-beF-60 52-beF-42 52-raM-41 52-rpA-10 52-rpA-91 52-yaM-70 52-yaM-52 52-nuJ-21 52-nuJ-03 52-luJ-81 52-guA-50 52-guA-32 52-peS-01 52-peS-82 52-tcO-61 52-voN-30 52-voN-12State of the Economy ARTICLE Chart II.6: Central Banks Opted for Status Quo in November Type Countries of previous rate cuts amidst significant downside Aggregate Demand risks to growth. In November so far, the UK, Sweden The high-frequency indicators of overall economic and Australia from the AEs and Brazil, Indonesia activity remained robust in October, supported by and China from the EMDEs kept the interest rates Goods and Services Tax (GST) rate reductions and a steady. Malaysia kept the policy rate unchanged over pickup in festive spending. Despite a reduction in steady economic growth. Central banks of Mexico and rates, GST collections registered a positive growth, South Africa, however, cut rates on growth concerns albeit at a slower pace than the previous month. (Chart II.6). Digital payments registered a moderation in growth, in both volume and value, during October 2025 (Table III. Domestic Developments III.1). Recent data on digital transactions also indicate The Indian economy showed signs of a further a rising adoption and usage of digital payments across pick up in momentum, despite continuing global regions and merchant categories, including groceries headwinds. Quarterly results of listed private and supermarkets, and gold purchases.5 Electricity companies in manufacturing and services show 5 Six of the seven north-eastern states recorded the highest growth in UPI payment volumes in October 2025, reflecting the deepening penetration of an uptick in sales growth. Available high-frequency digital payments across the nation. The increasing volume of transactions indicators for October suggest a robust expansion within the UPI Person-to-Merchant (P2M) category, reflects a robust growth in adoption of UPI for everyday payments. Within the UPI Person-to- in both manufacturing and services activities, Merchant (P2M) category, the volume of transactions under the ‘groceries and supermarkets’ and online marketplaces segment grew by close to supported by festive season demand and the ongoing 40 and 60 cent (y-o-y) in October 2025, respectively. Notably, digital gold purchases have featured among the top 20 merchant categories under UPI positive impact of the Goods and Services Tax (GST) P2M over the past six months, indicating evolving consumer preferences reforms. Inflation has moderated to a historic low in modes of buying gold (Source: Unified Payments Interface Ecosystem Statistics, NPCI and authors’ calculations. Retrieved on November 4, 2025, and remained well below the target rate. from https://www.npci.org.in/product/ecosystem-statistics/upi). RBI Bulletin November 2025 39 42-naJ 42-beF 42-raM 42-rpA 42-yaM 42-nuJ 42-luJ 42-guA 42-peS 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS 52-tcO voN ot pU 5202 ,12 Australia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Canada 0 0 0 0 0 0 0 0 0 -1 0 -1 0 0 0 0 0 0 0 0 0 0 0 Euro area 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Japan 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Advanced New Zealand 0 0 0 0 0 0 0 0 0 -1 -1 0 0 -1 0 0 0 0 0 0 0 -1 0 Economies South Korea 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Sweden 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 Switzerland 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 United Kingdom 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 United States 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Brazil -1 0 -1 0 0 0 0 0 0 0 1 1 1 0 1 0 1 0 0 0 0 0 0 China 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 India 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 Indonesia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Emerging Market Malaysia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 and Developing Mexico 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 -1 0 -1 -1 0 0 0 0 0 Economies Philippines 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Russia 0 0 0 0 0 0 2 0 1 2 0 0 0 0 0 0 0 -1 -2 0 -1 -1 0 Saudi Arabia 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 South Africa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Thailand 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rate change < -0.75 -0.75 to -0.50 -0.50 to -0.25 -0.25 to <0 0 (No change) >0 to 0.25 0.25 to 0.50 0.50 to 0.75 > 0.75 Colour Note: White-coloured blocks indicate an off-policy month. Source: Bloomberg.ARTICLE State of the Economy Table III.1: High Frequency Indicators – Robust Economic Activity Indicator Oct-24 Nov-24 Dec-24 Jan-25 Feb-25 Mar-25 Apr-25 May-25 Jun-25 Jul-25 Aug-25 Sep-25 Oct-25 GST E-way bills 16.9 16.3 17.6 23.1 14.7 20.2 23.4 18.9 19.3 25.8 22.4 21.0 8.2 GST revenue 8.9 8.5 7.3 12.3 9.1 9.9 12.6 16.4 6.2 7.5 6.5 9.1 4.6 Toll Collection 7.9 11.9 9.8 14.8 18.7 11.9 16.6 16.4 15.5 14.8 12.7 4.5 4.6 Electricity demand -0.4 3.7 5.1 1.3 2.4 5.7 2.8 -4.8 -2.3 2.6 3.8 3.4 -5.6 Petroleum consumption 4.1 10.6 2.0 3.0 -5.2 -3.1 0.2 1.1 0.5 -4.4 4.8 7.6 -0.4 Of which 8.7 9.6 11.1 6.7 5.0 5.7 5.0 9.2 6.8 5.9 5.5 8.0 7.4 Petrol Diesel 0.1 8.5 5.9 4.2 -1.3 0.9 4.2 2.1 1.5 2.4 1.2 6.6 -0.3 Aviation Turbine Fuel 9.4 8.5 8.7 9.4 4.2 5.7 3.9 4.4 3.3 -2.3 -2.9 -0.8 2.1 Digital Payments - volume 40.3 30.1 33.1 33.0 26.7 30.8 30.0 29.2 28.3 30.9 31.1 28.1 19.0 Digital Payments - value 27.5 9.5 19.6 18.6 9.5 17.3 18.4 12.6 17.4 16.6 5.3 13.4 9.1 <<Contraction --------------------------------------------------------------------------------------- Expansion>> Notes: 1. The y-o-y growth (in per cent) has been calculated for all indicators. 2. The heatmap is applied to data from April 2023 to October 2025. Digital Payments data for October 2025 is provisional. 3. The heatmap translates the data range for each indicator into a colour gradient scheme with red denoting the lowest values and green corresponding to the highest values of the respective data series. Sources: Goods and Services Tax Network (GSTN); RBI; Central Electricity Authority (CEA); and Ministry of Petroleum and Natural Gas, GoI. demand declined due to unseasonal rainfall and conditions, strong agricultural activity, GST rate the early onset of the winter season. Fuel demand reductions, and increased festive season spending. presented a mixed picture, with petrol consumption Rural demand for two-wheelers and automobiles rising due to increased mobility and travel during the registered a sharp pick up, as sales recorded the festive season, while diesel consumption showed a highest growth rate for both series. Urban demand marginal decline.6 also gained momentum, with passenger vehicle During October, overall demand conditions sales recording their highest growth in the past nine showed signs of improvement. Rural demand steered months (Table III.2). Vehicle registrations recorded overall demand, supported by favourable monsoon strong growth across all major segments compared Table III.2: High Frequency Indicators- Revival of Urban Demand Indicator Oct-24 Nov-24 Dec-24 Jan-25 Feb-25 Mar-25 Apr-25 May-25 Jun-25 Jul-25 Aug-25 Sep-25 Oct-25 Urban Domestic air passenger traffic 9.6 13.8 10.8 14.1 12.1 9.9 9.7 2.6 3.7 -2.5 -0.5 -2.5 2.8 demand Retail Passenger vehicle sales 32.4 -13.7 -2.0 15.5 -10.3 6.3 1.6 -3.1 2.5 -0.8 0.9 5.8 11.4 Retail Automobile Sales 32.1 11.2 -12.5 6.6 -7.2 -0.7 2.9 5.4 4.8 -4.3 2.8 5.2 40.5 Rural Retail Tractor sales 3.1 29.9 25.8 5.2 -14.5 -5.7 7.6 2.8 8.7 11.0 30.1 3.6 14.2 demand Retail Two-wheeler sales 36.3 15.8 -17.6 4.2 -6.3 -1.8 2.3 7.3 4.7 -6.5 2.2 6.5 51.8 <<Contraction ----------------------------------------------------------------------------------------- Expansion>> Notes: 1. The y-o-y growth (in per cent) has been calculated for all indicators. 2. The heatmap is applied to data from April 2023 to October 2025. 3. The heatmap translates the data range for each indicator into a colour gradient scheme with red denoting the lowest values and green corresponding to the highest values of the respective data series. 4. The data on domestic air passenger traffic for October 2025 growth rate is calculated by aggregating daily data. Sources: Airports Authority of India; Federation of Automobile Dealers Associations (FADA); and Ministry of Rural Development, GoI. 6 Petrol consumption increased to 3.6 million tonnes in October 2025 (five-month high) as compared to 3.4 million tonnes in October 2024. 40 RBI Bulletin November 2025State of the Economy ARTICLE of age 15 years and above) declined to 5.2 per cent Chart III.1: Surge in Automobile Registrations (Growth in per cent) in Q2 from 5.4 per cent in Q1 with divergent trends 30 across rural and urban areas. The unemployment 25 24.6 23.4 rate in rural areas declined while that in urban areas 22.7 increased slightly8. In October, based on the Monthly 20 18.9 Bulletin, the all-India unemployment rate remained 15.2 15 13.9 unchanged at 5.2 per cent, with a marginal decline 11.4 10 8.9 8.8 in rural areas, while the urban unemployment rate increased. Labour force participation rate and worker 5 population ratio increased to their highest level 0.4 0 since May, driven by gains in rural areas. The PMI Two wheelers Passenger Three Commercial Total vehicles wheelers employment indices for both manufacturing and 2024 2025 services remained within the expansionary zone. Note: Growth in registration calculated for 45 day festive period beginning with the first day of Navratri, i.e., (September 22 – November 5) in 2025; 2024: October 2 - November 15 in 2024; October 15 – November 28 in 2023. The Naukri JobSpeak Index experienced contraction Source: Vahan dashboard, Ministry of Road Transport & Highways (MoRTH). in October, reflecting subdued momentum in white- with the corresponding festive period last year7, collar hiring activity, partly due to the clustering of reflecting strong consumer sentiment and the major festive holidays in the month. Meanwhile, the positive impact of GST rate cuts (Chart III.1). continuation of contraction in work demand under As per the Quarterly Bulletin of the Periodic the Mahatma Gandhi National Rural Employment Labour Force Survey (released on November 10, Guarantee Scheme (MGNREGS) suggests improving 2025), all India unemployment rate (among persons rural labour market conditions (Table III.3). Table III.3: Robustness in High Frequency Indicators for Employment Indicator Oct-24 Nov-24 Dec-24 Jan-25 Feb-25 Mar-25 Apr-25 May-25 Jun-25 Jul-25 Aug-25 Sep-25 Oct-25 Unemployment rate (PLFS: All-India) 5.1 5.6 5.6 5.2 5.1 5.2 5.2 Unemployment rate (PLFS: Rural) 4.5 5.1 4.9 4.4 4.3 4.6 4.4 Unemployment rate (PLFS:Urban) 6.5 6.9 7.1 7.2 6.7 6.8 7.0 Naukri JobSpeak Index 10.0 2.0 8.7 3.9 4.0 -1.5 8.9 0.3 10.5 6.8 3.4 10.1 -9.3 PMI Employment: Manufacturing 53.3 52.9 53.4 54.8 54.5 53.4 54.2 54.9 55.1 53.3 53.1 52.1 52.4 PMI Employment: Services 54.3 56.6 55.5 56.3 56.2 52.5 53.9 57.1 55.1 51.4 52.2 51.9 51.4 MGNREGA: Work Demand -7.6 3.9 8.2 14.4 2.8 2.2 -6.5 4.4 4.4 -12.3 -26.2 -27.1 -35.1 <<Contraction --------------------------------------------------------------------------------------------- Expansion>> Notes: 1. All PLFS indicators are in the current weekly status and for people aged 15 years and above. 2. The y-o-y growth (in per cent) has been calculated for the Naukri index. 3. The heatmap is applied to data from April 2023 to October 2025. 4. The heatmap translates the data range for each indicator into a colour gradient scheme with red denoting the lowest values and green corresponding to the highest values of the respective data series. 5. All PMI values are reported in index form. A PMI value >50 denotes expansion, <50 denotes contraction and =50 denotes ‘no change’. In the PMI heatmaps, red denotes the lowest value, yellow denotes 50 (or the no change value), and green denotes the highest value in each of the PMI series. Sources: Ministry of Statistics and Program Implementation (MoSPI), GoI; Info Edge; and S&P Global. 7 The period from September 22 to November 5 has been considered to capture the entire festive season (45 days), beginning with the implementation of GST 2.0 on September 22 - which coincided with the first day of Navratri - and extending through the post-Diwali period. For comparability, the corresponding festive periods in previous years have been taken as October 2 – November 15, 2024, and October 15 – November 28, 2023. 8 Further, labour force participation rate in Q2: 2025-26 recorded a modest uptick in both rural and urban areas and worker population ratio rose marginally driven by increased participation of women, particularly in rural areas. RBI Bulletin November 2025 41ARTICLE State of the Economy During H1:2025-26 (April-September 2025), the the collection of state goods and services tax and sales gross fiscal deficit (GFD) of the union government was tax/VAT moderated, state excise and non-tax revenue higher than the corresponding period of the previous registered robust growth. There was also a contraction year (Chart III.2).9 This was due to higher growth of in grants-in-aid from the centre. On the expenditure capital expenditure10 accompanied by a contraction front, revenue expenditure growth was moderate, in the net tax receipts11. The growth in revenue while capital expenditure rebounded sharply. expenditure, on the other hand, decelerated12. The During the year so far (April-October), the decline in net tax receipts reflected a deceleration merchandise trade deficit was higher than last year, in growth for both direct and indirect taxes13. The primarily driven by the gold as well as the non-oil non- slowdown in net tax collections during the period was gold deficit. India’s merchandise exports witnessed a partially offset by robust growth in non-tax revenue and non-debt capital receipts. marginal expansion supported by electronic goods, even as imports surged.14 The key deficit indicators of states during H1:2025- 26 were higher than last year (Chart III.2b). This was In October, the merchandise trade deficit widened largely on account of subdued revenue growth. While to an all-time high15 in (Chart III.3a)16. While exports Chart III.2: Deficit Indicators Higher than Previous Year (April-September) a. Union Government b. State Governments (Actuals as per cent of budget estimates) (Actuals as a per cent of budget estimates) 40 36.5 140 131.7 29.4 120 30 102.4 100 20 12.8 80 10 5.2 60 0 40 37.1 37.6 -1.7 29.4 30.4 -10 -9.0 20 -20 0 Revenue deficit Gross fiscal deficit Primary deficit Revenue deficit Gross fiscal deficit Primary deficit 2024-25 2025-26 2024-25 2025-26 Note: Negative primary deficit numbers, as per cent of budget estimates, in Chart 1a indicate primary surplus. In Chart b, data pertains to 23 States/UTs. Sources: Controller General of Accounts; Comptroller and Auditor General of India; and Union Budget Documents. 9 As per the latest data released by the Controller General of Accounts (CGA). 10 The growth in capital expenditure remained robust at 40.0 per cent and attained 51.8 per cent of its budgeted target for 2025-26 during the first half of the year. 11 Net tax collections registered contraction as the increase in gross tax revenue during the period was more than offset by devolution of tax from Centre to the States. 12 The revenue expenditure recorded a moderate growth of 1.5 per cent during H1:2025-26. The interest payments grew by 12.3 per cent, while the spending on major subsidies contracted by 5.7 per cent during the same period. 13 The direct and indirect tax growth decelerated from 14.8 per cent and 8.4 per cent in H1:2024-25 to 3.0 per cent and 2.6 per cent in H1: 2025-26, respectively. Within major indirect taxes, only union excise duty registered an acceleration in growth. 14 Electronic goods exports at US$26.3 billion have posted a robust growth of 37.8 per cent (y-o-y) during 2025-26 (April-October) 15 US$41.7 billion in October 2025 from US$26.2 billion in October 2024. 16 The non-oil deficit increased to US$30.8 billion in October 2025, as compared to US$11.8 billion a year ago due to a rise in gold deficit. The share of non-oil deficit in total deficit increased to 74.0 per cent in October 2025 from 44.8 per cent a year ago. 42 RBI Bulletin November 2025State of the Economy ARTICLE contracted after remaining in expansion for three Aggregate Supply months, reflecting the adverse impact from global Agriculture headwinds, imports surged on account of higher gold The final estimates for agricultural production and silver imports catering to the festive demand in 2024–25 indicate a record output of foodgrains, led by higher production of rice, wheat, maize and (Chart III.3b)17. moong. Oilseeds output also increased, supported by Net services exports growth accelerated in gains in groundnut and soybean.20 September18 with services exports growing faster Excess rainfall conditions were witnessed and imports emerging out of contraction19. Services in the post-monsoon period this year owing to exports were driven by business services and software Chart III.4: Trend in Services Exports and Imports services exports (Chart III.4). Per cent (y-o-y) 35 30 17 Merchandise exports stood at US$34.4 billion in October 2025 [decline 25 of 11.8 per cent (y-o-y)]. Key segments such as engineering goods, gems 20 and jewellery; chemicals, petroleum products and plastic and linoleum 15 drove the contraction, while electronic goods; meat, dairy and poultry 12.5 products; marine products, cashew and coffee performed well. Exports to 10 7.8 17 out of top 20 countries contracted, with exports to destinations such as 5 China and Hong Kong growing, while contracting to the US, the UAE and 0 the Netherlands. Merchandise imports stood at US$76.1 billion in October -5 2025 [growth of 16.6 per cent (y-o-y)]. Gold, silver, electronic goods, -10 fertilisers, crude and manufactured; and machinery, electrical and non- -15 electrical were the major drivers contributing to the increase in import growth during the month. Petroleum, crude and products, iron and steel; pearls, precious and semi-precious; coal, coke and briquettes; and pulses Exports Imports dragged imports down. Source: RBI. 18 Net services exports growth accelerated to 17.3 per cent (y-o-y), reaching US$ 18.8 billion. 19 Services imports registered annual growth of 7.8 per cent in September, primarily due to a rise in software services and business services imports. RBI Bulletin November 2025 43 32-luJ 32-guA 32-peS 32-tcO 32-voN 32-ceD 42-naJ 42-beF 42-raM 42-rpA 42-yaM 42-nuJ 42-luJ 42-guA 42-peS 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS Chart III.3: India’s Merchandise Trade a. Merchandise Trade Deficit Widened to b. Exports Contracted while Imports Surged All-time High in October 2025 (Y-o-y, per cent) (US$ billion) 100 80 76.1 60 40 34.4 20 0 -20 -40 -41.7 -60 Exports Imports Trade balance Sources: PIB; and DGCI&S. 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS 52-tcO 25 20 16.6 15 10 5 0 -5 -10 -15 -11.8 -20 Imports Exports 32-peS 32-voN 42-naJ 42-raM 42-yaM 42-luJ 42-peS 42-voN 52-naJ 52-raM 52-yaM 52-luJ 52-peS 20 Final estimates were at 357.7 million tonnes, which was 7.7 per cent higher than last year. Oilseeds production grew by 8.4 per cent driven by record production of groundnut and soybean.ARTICLE State of the Economy Chart III.5: Reservoir Level Surged with Excess Post Monsoon Rainfall a. Progress of Post Monsoon Rainfall b. Record High Reservoir Level (Cumulative per cent deviation over normal) (per cent of full reservoir level) 60 40 26 20 0 -20 -12 -40 -60 2024 2025 Last 10 years average 2024 2025 Note: Reservoir levels as on November 20, 2025. Sources: India Meteorological Department; and Central Water Commission. cyclone Montha and depression in the Arabian and coarse cereals recording higher sowing so far sea, which is in sharp contrast to the shortfall (Chart III.6).22 witnessed last year (Chart III.5a). Consequently, Aided by higher production and lower market the average storage level in major reservoirs in the price, procurement of rice during the kharif country has reached a historic high compared to the marketing year 2025-26 so far (September 10 to corresponding period in the previous years, which November 21, 2025) has surpassed last year’s level.23 augurs well for the ongoing rabi season sowing Consequently, the stock of rice with the Food (Chart III.5b).21 Corporation of India has reached its highest level Rabi season sowing is progressing well in recent years.24 The stock of wheat also remains across crops with wheat, rice, pulses, oilseeds comfortable at 1.5 times the buffer requirement (Chart III.7). Industry and Services Quarterly results of listed non-government non-financial25 companies for Q2:2025-26 show an uptick.26 Sales growth of listed private manufacturing companies inched up despite a 22 With rabi sowing at 306.3 lakh hectares, around 48 per cent of normal area has been covered so far. As on November 21, 2025, sowing is 12.3 per cent higher than the corresponding date last year. 23 Procurement of rice during the kharif marketing year 2025-26 so far (September 10 to November 21, 2025) has been 12.6 per cent higher than the corresponding period last year. 24 As on November 01, 2025, the stock of rice is 5.3 times the buffer requirement. 25 Based on 3,118 listed non-government non-financial companies. 26 Aggregate sales growth increased to 8 per cent (y-o-y) during Q2:2025-26 from 5.5 per cent in the previous quarter. 44 RBI Bulletin November 2025 10 tcO 30 tcO 50 tcO 70 tcO 90 tcO 11 tcO 31 tcO 51 tcO 71 tcO 91 tcO 12 tcO 32 tcO 52 tcO 72 tcO 92 tcO 13 tcO 2 voN 4 voN 6 voN 8 voN 01 voN 21 voN 41 voN 61 voN 81 voN 02 voN 100 96 91 89 90 85 86 83 81 80 73 70 60 50 40 nrehtroN nretsaE nretseW lartneC nrehtuoS aidnI llA Chart III.6: Progress of Rabi Sowing (Lakh hectares) 140 120 100 80 60 40 20 0 2024-25 2025-26 Note: Data is as on November 21. Source: Ministry of Agriculture and Farmers’ Welfare. eciR esraoC slaerec sesluP sdeesliO taehW 21 As on November 20, 2025, the average storage level in 161 major reservoirs in the country has reached 89 per cent of its full capacity.State of the Economy ARTICLE Chart III.7: Procurement and Stock of Chart III.8: Sales Growth - Listed Private Rice and Wheat Non-Financial Companies (Lakh tonnes) (Y-o-y, per cent) 600 15 543 500 441 10.6 400 10 8.5 300 307 300 266 7.8 223 200 172 5 152 100 0 - Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 2024-25 2025-26 2023-24 2024-25 Rice Wheat 2023-24 2024-25 2025-26 Procurement Stock Manufacturing IT Services (Non-IT) Notes: 1. Rice procurement from September 10 to November 21 and wheat procurement from April to June. Note: Data is based on results of 3,118 listed non-government non-financial 2. Stock as on November 01. companies (1,775 manufacturing, 201 IT, and 890 non-IT services). Source: Food Corporation of India. Sources: Capitaline database; and RBI staff calculations. contraction in the petroleum industry.27 Sales growth Within the services sector, operating profit and of IT companies and non-IT services companies also operating profit margin of IT companies expanded inched up (Chart III.8). sequentially, aided by cost rationalisation, but they moderated for non-IT services companies Operating profit and operating profit margin of (Chart III.9). manufacturing companies have risen on a year-on- year basis during Q2:2025-26. However, operating During Q2:2025-26, revenue growth for listed profit margin moderated from the previous quarter. Indian banking and financial sector companies Chart III.9: Profitability of Listed Private Non-Financial Companies a. Operating Profit Growth b: Operating Profit Margin (Per cent, y-o-y) (Per cent) 25 12 11.3 10.6 22.1 10 20 19.7 7.7 8 15 6.9 14.2 6.5 6 5.4 10 4 5 2 0 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 0 Manufacturing IT Services (Non-IT) 2023-24 2024-25 2025-26 Q1:2025-26 Q2:2025-26 Manufacturing IT Services (Non-IT) Note: Data is based on results of 3,118 listed non-government non-financial companies (1,775 manufacturing, 201 IT, and 890 non-IT services). Sources: Capitaline database; and RBI staff calculations. 27 Sales growth of listed private manufacturing companies improved to 8.5 per cent (y-o-y) during Q2:2025-26 from 5.3 per cent in the previous quarter. Excluding petroleum, sales growth stood at 10 per cent during Q2:2025-26 RBI Bulletin November 2025 45ARTICLE State of the Economy moderated, while net profit growth increased industries remained unchanged, as growth in steel, (Chart III.10). cement, fertilisers and refinery products was offset by contractions in coal, electricity, natural gas and crude On the investment front, the total cost of capex oil. projects sanctioned by banks and financial institutions (FIs) during Q2:2025-26 surged over the previous The available high-frequency indicators for quarter pointing to improved investment optimism October point to sustained strength in manufacturing among private corporates. Power, construction, roads activity. The manufacturing Purchasing Managers’ and bridges constituted the majority of the intended Index (PMI) accelerated during the month, supported investment. Funds raised for capex through external by a sharp expansion in output and new orders, commercial borrowings (ECBs) and initial public underpinned by resilient domestic demand and the offerings (IPOs) also increased compared to the continued positive effects of Goods and Services preceding quarter (Chart III.11). Tax (GST) reforms. Steel output grew strongly, Monthly Indicators of Industrial Activity reflecting continued momentum in infrastructure and construction activity. Automobile production showed In September, growth in industrial activity, as mixed signals as passenger vehicle segment and three measured by the year-on-year change in the Index of wheelers recorded robust growth while production of Industrial Production (IIP), was more or less steady. two-wheeler declined (Table III.4). While electricity continued to expand, mining activity contracted after strong growth in the previous Green energy loans are gaining traction in India month. Growth in manufacturing activity picked as bank credit to the renewable energy sector showed up. Infrastructure/construction goods and consumer a triple digit growth in September (y-o-y) driven by durables were the best performers with double-digit consistent policy support as well as growing investor growth. In October, the combined index of eight core and consumer demand (Chart III.12). According to Chart III.10: Performance of Listed Chart III.11: Private Corporates’ Investment Financial Companies Intentions (Y-o-y growth in per cent) (₹ crore) 20 1,80,000 1,60,000 18 1,40,000 16 1,20,000 14 1,00,000 12 80,000 10 60,000 40,000 8 6.4 20,000 6 4.8 0 4 3.5 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 2.3 2 2023-24 2024-25 (P) 2025-26 (P) 0 Total cost of projects sanctioned by banks/FIs Revenue Expenditure Operating Profit Net Profit ECBs/ IPOs (Only capex) Dec 2024 Mar-25 Jun-25 Sep-25 Note: Data for 2024-25 and 2025-26 are provisional. Sources: Data on project finance gathered from banks/FIs by RBI; and RBI staff Sources: CMIE prowess; and RBI staff estimates. estimates. 46 RBI Bulletin November 2025State of the Economy ARTICLE Table III.4: High Frequency Indicators for Industry Showed Robust Growth Indicator Oct-24 Nov-24 Dec-24 Jan-25 Feb-25 Mar-25 Apr-25 May-25 Jun-25 Jul-25 Aug-25 Sep-25 Oct-25 IIP-Headline 3.7 5.0 3.7 5.2 2.7 3.9 2.6 1.9 1.5 4.3 4.1 4.0 IIP Manufacturing 4.4 5.5 3.7 5.8 2.8 4.0 3.1 3.2 3.7 6.0 3.8 4.8 IIP capital goods 2.9 8.9 10.5 10.2 8.2 3.6 14.0 13.3 3.0 6.8 4.5 4.7 PMI Manufacturing 57.5 56.5 56.4 57.7 56.3 58.1 58.2 57.6 58.4 59.1 59.3 57.7 59.2 PMI Export Order 53.6 54.6 54.7 58.6 56.3 54.9 57.6 56.9 60.6 57.3 56.1 56.5 54.7 PMI Manufacturing: Future Output 62.1 65.5 62.5 65.1 64.9 64.4 64.6 63.1 62.2 57.6 60.5 64.8 62.3 Eight Core Index 3.8 5.8 5.1 5.1 3.4 4.5 1.0 1.2 2.2 3.7 6.5 3.3 0.0 Electricity generation: Conventional 0.5 2.7 4.5 -1.3 2.4 4.8 -1.8 -8.2 -6.1 -0.8 1.0 0.8 -10.8 Electricity generation: Renewable 14.9 19.0 17.9 31.9 12.2 25.2 28.0 18.2 28.7 26.4 22.7 16.4 Automobile Production 10.0 8.0 1.3 9.4 2.3 6.5 -1.7 5.2 1.2 10.7 8.1 10.8 -2.8 Passenger vehicle production -4.0 6.5 9.2 3.7 4.5 11.2 10.8 5.4 -1.8 0.1 -4.1 16.1 9.8 Tractor production 0.4 24.7 20.9 23.7 -7.8 18.5 20.5 9.1 9.8 11.5 9.4 23.0 13.0 Two-wheelers production 13.3 8.8 -0.6 10.3 1.6 5.6 -4.1 4.7 1.4 12.3 10.0 9.8 -5.6 Three-wheelers production -6.7 -5.5 7.6 16.2 6.5 6.0 4.1 16.9 8.6 24.0 15.8 15.9 15.9 Crude steel production 4.2 4.5 8.3 7.4 6.0 8.5 9.3 11.0 12.6 13.8 12.8 13.2 9.4 Finished steel production 4.0 2.8 5.3 6.7 6.7 10.0 6.6 7.0 10.9 13.8 13.8 13.8 10.0 Import of capital goods 7.0 4.8 6.1 15.5 -0.5 8.6 24.6 15.7 3.4 12.0 -1.4 10.1 8.7 <<Contraction ----------------------------------------------------------------------------------------- Expansion>> Notes: 1. The y-o-y growth (in per cent) has been calculated for all indicators (except for PMI). 2. The heatmap translates the data range for each indicator into a colour gradient scheme with red denoting the lowest values and green corresponding to the highest values of the respective data series. 3. The heatmap is applied on data from April 2023 till October 2025, other than for the IIP and electricity generation: renewable, where the data are till September 2025. 4. All PMI values are reported in index form. A PMI value >50 denotes expansion, <50 denotes contraction and =50 denotes ‘no change’. In the PMI heatmaps, red denotes the lowest value, yellow denotes 50 (or the no change value), and green denotes the highest value in each of the PMI series. Sources: Ministry of Statistics and Programme Implementation (MoSPI); S&P Global; Central Electricity Authority (CEA), Ministry of Power; Society of Indian Automobile Manufacturers (SIAM); Office of Economic Adviser, GoI; Joint Plant Committee; Directorate General of Commercial Intelligence & Statistics; and Tractor and Mechanisation Association. the International Energy Agency (IEA)28, India’s clean Chart III.12: Deployment of Gross Bank Credit (Y-o-y, per cent) energy expansion is on track with its renewables 140 25 market expected to emerge as the world’s second 119 120 largest by 2030.29 20 100 Electric vehicles industry has been showcasing 15 impressive growth with the total EV registrations 80 reaching an all-time high in October. Within the 60 10 EV segment, electric two-wheeler category crossed 7 10 40 the milestone of one million sales during 2025 so 5 far reflecting a transformative shift towards green 20 18 mobility. 0 0 28 Renewables Report – October 2025, International Energy Agency. 29 As at end-September, India’s non-fossil energy capacity cumulatively stood at 256 gigawatts (GW) after adding about 28 GW of additional installed capacity in the first half of 2025-26 as compared to 11 GW during Source: RBI. the corresponding period of the previous year. RBI Bulletin November 2025 47 12-peS 22-peS 32-peS 42-peS 52-peS Gross Bank Credit to Renewable Energy Total Bank Credit (RHS)ARTICLE State of the Economy Table III.5: High Frequency Indicators for Services Showed Resilience Indicator Oct-24 Nov-24 Dec-24 Jan-25 Feb-25 Mar-25 Apr-25 May-25 Jun-25 Jul-25 Aug-25 Sep-25 Oct-25 PMI Services 58.5 58.4 59.3 56.5 59.0 58.5 58.7 58.8 60.4 60.5 62.9 60.9 58.9 International Air Passenger Traffic 10.3 10.7 9.0 11.1 7.7 6.8 13.0 5.0 3.4 5.5 7.7 7.3 9.6 Domestic Air Cargo 8.9 0.3 4.3 6.9 -2.5 4.9 16.6 2.3 2.6 4.8 7.1 2.8 International Air Cargo 18.4 16.1 10.5 7.1 -6.3 3.3 8.6 6.8 -1.2 4.2 4.5 2.3 Port Cargo Traffic -3.4 -5.0 3.4 7.6 3.6 13.3 7.0 4.3 5.6 4.0 2.5 11.5 12.0 Retail Commercial vehicle sales 6.4 -6.1 -5.2 8.2 -8.6 2.7 -1.0 -3.7 6.6 0.2 8.6 2.7 17.7 Hotel Occupancy -5.3 11.1 -0.2 1.2 0.6 1.9 7.2 -2.8 -0.3 -2.4 -3.2 -0.6 Steel Consumption 8.1 9.5 5.2 10.9 10.9 13.6 6.0 8.1 9.3 7.3 10.0 8.9 4.7 Cement Production 3.1 13.1 10.3 14.3 10.7 12.2 6.3 9.7 8.2 11.6 5.4 5.0 5.3 <<Contraction --------------------------------------------------------------------------------------------- Expansion>> Notes: 1. The y-o-y growth (in per cent) has been calculated for all indicators (except for PMI). 2. The heatmap translates the data range for each indicator into a colour gradient scheme with red denoting the lowest values and green corresponding to the highest values of the respective data series. 3. The heatmap is applied to data from April 2023 to October 2025, other than for domestic and international air cargo, and hotel occupancy, where the data are till September 2025. 4. The data on international air passenger traffic for October 2025 growth rate is calculated by aggregating daily data. 5. All PMI values are reported in index form. A PMI value >50 denotes expansion, <50 denotes contraction and =50 denotes ‘no change’. In the PMI heatmaps, red denotes the lowest value, yellow denotes 50 (or the no change value), and green denotes the highest value in each of the PMI series. Sources: Federation of Automobile Dealers Associations (FADA); Indian Ports Association; Airports Authority of India; HVS Anarock; Joint Plant Committee; Office of Economic Adviser; and S&P Global. Monthly Indicators of Services Activity deepening of deflation in food prices and impact of the GST rate cut on goods and services prices, amid India’s services sector demonstrated sustained large favourable base effects (Chart III.13).31 expansionary momentum, supported by robust festive demand and GST relief. However, unseasonal The deflation in the food group deepened on rains led to some sequential softening in activity in account of a decline in the prices of vegetables, pulses October. Sales of retail commercial vehicles rose to and spices.32 Inflation in sub-groups such as cereals, near three-year high. Growth in port traffic accelerated, meat and fish, milk and products, eggs, oils and fats, led by an uptick in petroleum, oil & lubricants and fruits, prepared meals, and non-alcoholic beverages containerised cargo. Growth in cement production moderated (Chart III.14). improved, while steel consumption moderated (Table Fuel and light inflation remained at 2.0 per III.5). cent in October, the same as in September. Inflation Inflation continued to remain elevated for LPG while it remained low and steady in electricity. Headline CPI inflation declined in October to touch an all-time low in the current (2012=100 base Core (i.e., CPI excluding food and fuel) inflation year) series. Headline CPI inflation, moderated to moderated to 4.3 per cent in October 2025 from 0.3 per cent in October 2025 from 1.4 per cent in September.30 The fall in inflation was driven by the 31 The decline in inflation by about 120 bps was on account of a large favourable base effect of around (-)135 bps which offset a positive price momentum of around 15 bps. 30 As per the provisional data released by the National Statistics Office 32 Food deflation deepened to 3.7 per cent in October from 1.4 per cent in (NSO) on November 12, 2025. the previous month. 48 RBI Bulletin November 2025State of the Economy ARTICLE Chart III.13: Easing Food Inflation Drove the Decline in Headline Inflation a. CPI Inflation b. Contibutions (Y-o-y, per cent) (Percentage points) 12 10 8 6 4 4.3 2 2.0 0 0.3 -2 -4 -3.7 -6 Food and beverages CPI excluding food and fuel Fuel and light CPI Headline (y-o-y, per cent) Sources: National Statistics Office (NSO); and RBI staff estimates. 4.4 per cent in September driven by clothing and effects subgroups recorded an increase in inflation. footwear, health, recreation and amusement, and Excluding precious metals, the core component transport and communication subgroups. Education, recorded a decline of 0.1 per cent from the previous pan, tobacco and intoxicants, and personal care and month, reflecting the impact of GST rate cuts. RBI Bulletin November 2025 49 42-tcO 42-ceD 52-beF 52-rpA 52-nuJ 52-guA 52-tcO 8 7 6 5 4 3 2 1 0 0.3 -1 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS 52-tcO Food and beverages CPI excluding food and fuel Fuel and light CPI Headline (y-o-y, per cent) Chart III.14: Key Drivers of the Decline in Inflation: Vegetables, and Oils and Fats (Y-o-y, Per cent) Sources: NSO; and RBI staff estimates.ARTICLE State of the Economy Inflation in both rural and urban areas eased in Chart III.15: Moderation in State-level CPI Inflation October with the former registering deflation of 0.3 (Y-o-y, per cent) per cent while the latter recording inflation of 0.9 per cent. While inflation ranged from (–) 2.9 per cent to 8.6 per cent across states/UTs, majority of states recorded inflation below 2 per cent. Overall, a broad-based moderation in state-level inflation was observed, as it declined or remained stable in 35 out of 37 states/UTs (Chart III.15). High-frequency food price data for November so far (up to 21st) point towards a moderation in Inflation Number of Inflation Number of cereal prices. Among pulses, prices moderated for Range States/UTs Trend States/UTs <2 29 Decline or Stable 35 gram and moong dal while it increased marginally 2-4 6 Increase 2 for tur/arhar dal. Within edible oils, sunflower oil 4-6 0 6-8 1 prices increased, groundnut oil prices eased, and 8-10 1 mustard oil prices stayed stable. Prices of key vegetables Notes: 1. Map is for illustrative purposes only. 2. Lakshadweep and Kerala experienced above 6 per cent inflation. (tomato, onion, and potato) hardened (Chart III.16). Sources: NSO; and RBI Staff estimates. Chart III.16: Food Price Remained Stable in November a. Cereals b. Pulses Index (Jan 2024 = 100) Index (Jan 2024 = 100) 115 110 105 100 95 90 Wheat Rice Gram dal Tur/ Arhar dal Moong dal c. Edible Oils d. Vegetables Index (Jan 2024 = 100) Index (Jan 2024 = 100) Mustard oil Sunflower oil Groundnut oil Potato Onion Tomato Sources: Department of Consumer Affairs, GoI; and RBI staff estimates. 50 RBI Bulletin November 2025 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS 52-tcO 52-voN 105.1 104.8 95.2 77.1 100 250 129.5 200 121.2 151.0 150 100 122.5 100.7 50 71.0 0 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS 52-tcO 52-voN 130 125 120 115 110 105 100 95 90 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS 52-tcO 52-voN 120 115 110 105 100 95 90 85 80 75 70 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS 52-tcO 52-voN <2 2-4 6-8 8-10State of the Economy ARTICLE IV. Financial Conditions Table III.6: Petroleum Products Prices Item Unit Domestic Prices Month-over- Overall financial conditions remained benign month (per cent) in October and November (up to 21st), primarily due Nov-24 Oct-25 Nov-25^ Oct-25 Nov-25^ to easing in the equity and corporate bond markets Petrol ₹/litre 100.99 101.1 101.1 0.0 0.0 Diesel ₹/litre 90.45 90.5 90.5 0.0 0.0 (Chart IV.1). Kerosene ₹/litre 43.4 45.4 45.4 3.4 0.0 System liquidity remained in surplus during the (subsidised) LPG (non- ₹/cylinder 813.3 863.3 863.3 0.0 0.0 second half of October and November (up to 21st), subsidised) though temporary increases in government cash ^: For the period November 1-21, 2025. Note: Other than kerosene, prices represent the average Indian Oil balances and a rise in currency-in-circulation due Corporation Limited (IOCL) prices in four major metros (Delhi, Kolkata, Mumbai and Chennai). For kerosene, prices denote the average of to festival-related demand led to some episodes subsidised prices in Kolkata, Mumbai and Chennai. of liquidity deficit in the second half of October. Sources: IOCL; Petroleum Planning and Analysis Cell (PPAC); and RBI staff estimates. Overall, average net absorption under the liquidity adjustment facility increased marginally to ₹1.3 Retail selling prices of petrol, diesel, kerosene lakh crore during October 16 to November 21, and LPG remained unchanged in November (up to from ₹1.0 lakh crore in the preceding one-month 21st) [Table III.6]. period, supported by CRR cuts (Chart IV.2). To In October, the PMIs for manufacturing recorded offset the transient liquidity tightness during this a moderation in the rate of expansion of both period, the Reserve Bank conducted 11 variable rate input and output prices with a sharper fall in the repo auctions (overnight to 7-day maturity). With former. For services, deceleration was seen in both an improvement in overall liquidity conditions, input prices and selling prices due to GST reforms a 3-day VRRR was conducted on November 14 (Chart III.17). which absorbed surplus liquidity of around ₹0.57 lakh Chart III.17: Pace of Input and Output Price Expansion eased for both Manufacturing and Services Firms a. Manufacturing b. Services Index (50=No Change) Index (50=No Change) 60 55.1 55 51.5 50 45 Input Prices Output Prices Input Prices Prices Charged Note: A level of 50 corresponds to no change in activity, and a reading above 50 denotes expansion and vice versa. Source: S&P. RBI Bulletin November 2025 51 32-tcO 32-ceD 42-beF 42-rpA 42-nuJ 42-guA 42-tcO 42-ceD 52-beF 52-rpA 52-nuJ 52-guA 52-tcO 60 55 52.2 50 45 32-tcO 32-ceD 42-beF 42-rpA 42-nuJ 42-guA 42-tcO 42-ceD 52-beF 52-rpA 52-nuJ 52-guA 52-tcO 52.2ARTICLE State of the Economy crore against the notified amount of ₹1.0 lakh Money Market crore. Average balances under the standing deposit The weighted average call rate (WACR) remained facility remained marginally higher, and banks’ broadly aligned with the policy repo rate in October recourse to the marginal standing facility remained and November, despite some temporary liquidity unchanged.34 squeezes in the second half of October. The WACR Chart IV.2: Comfortable Liquidity Conditions (₹ lakh crore) 4.5 3.5 2.5 1.5 0.5 -0.5 -1.5 -2.5 -3.5 -4.5 Daily standing deposit facility Variable rate reverse repo Marginal standing facility Variable rate repo Net liquidity adjustment facility Total absorption Source: RBI. 33 For detailed methodology see https://rbi.org.in/Scripts/BS_ViewBulletin.aspx?Id=23451 34 Balances under the standing deposit facility increased modestly to ₹1.6 lakh crore during October 16 to November 21, 2025 from ₹1.4 lakh crore in the preceding one-month period. Borrowings from the marginal standing facility averaged ₹0.03 lakh crore during this period. 52 RBI Bulletin November 2025 52-naJ-01 52-naJ-13 52-beF-12 52-raM-41 52-rpA-40 52-rpA-52 52-yaM-61 52-nuJ-60 52-nuJ-72 52-luJ-81 52-guA-80 52-guA-92 52-peS-91 52-tcO-01 52-tcO-13 52-voN-12 Chart IV.1: Benign Financial Conditions (Standard deviation from average since 2012) 1.0 0.8 0.6 0.4 0.2 0.0 -0.2 -0.4 -0.6 -0.8 -1.0 Money Government securities Corporate bond Equity Foreign exchange Financial conditions index (standardised) Note: The financial conditions index, when at zero, corresponds to a financial system operating at the historical average level of all the financial indicators included in the index. To present the results, a standardised index is used.33 Source: RBI staff estimates. 52-naJ-01 52-naJ-13 52-beF-12 52-raM-41 52-rpA-40 52-rpA-52 52-yaM-61 52-nuJ-60 52-nuJ-72 52-luJ-81 52-guA-80 52-guA-92 52-peS-91 52-tcO-01 52-tcO-13 52-voN-12 Tighter conditions Easier conditionsState of the Economy ARTICLE Chart IV.3: Money Market Rates Remained Stable a. Policy Corridor and Call Rate b. Money Market Rates (Per cent) (Per cent) 8.5 8.0 7.5 7.0 6.5 6.10 6.0 5.99 5.5 5.34 5.0 4.5 Repo rate Weighted average call rate Standing deposit facility Marginal standing facility 3-month treasury bill 3-month certificate of deposit Secured Overnight Rupee Rate 3-month commercial paper (NBFC) Sources: RBI; and Bloomberg. hovered within the policy corridor as liquidity Government Securities (G-Sec) Market conditions improved since the beginning of In the fixed income segment, the yield curve November. Overall, the WACR remained unchanged slightly shifted upwards especially at the longer end, on an average at 5.5 per cent during October 16 to during the second half of October and into November (up to 21st).37 The average term spread (the difference November 21, 2025, compared with the preceding between the yields of 10-year G-sec and 91-day one-month period (Chart IV.3a). Overnight rates in treasury bill) inched up marginally during the period the collateralised segments – as measured by the (Charts IV.4a and IV.4b).38 benchmark secured overnight rupee rate – moved Corporate Bond Market in tandem with the uncollateralised rate. Yields on three-month commercial papers issued by non- Corporate bond yields generally softened across tenors and rating spectrum (Table IV.1). New banking financial companies averaged around the corporate bond issuances increased in September same level. At the same time, interest rates on compared to August. On a cumulative basis, too, certificates of deposit edged up slightly on account of total issuances were marginally higher in the rising credit-deposit ratio, while that on treasury bills current financial year compared to the previous moderated (Chart IV.3b).35 The average risk premium year.39 in the money market (the spread between the yields on 3-month commercial paper and 91-day treasury 37 The yield on the 10-year benchmark G-sec (6.33 per cent GS 2035) bill) recorded a mild uptick.36 firmed to 6.57 per cent on November 21, 2025, as against 6.48 per cent on October 15. 38 The average term spread between the 10-year G-sec and 91-day treasury 35 The average yields on 3-month certificates of deposit hardened by 6 bps bill increased by around 3 bps during October 16 to November 21, 2025 as during October 16 to November 21, 2025, as compared to the period from compared to the period from September 16 to October 15, 2025. September 16 to October 15, 2025. During the same period, the average 39 Increased to ₹0.74 lakh crore in September 2025, compared to ₹0.43 yields on 3-month treasury bills decreased by 3 bps. lakh crore in August 2025. On a cumulative basis (April to September), 36 Rose to 102 bps during the period from 16 to November 21, 2025, from it stood at ₹4.7 lakh crore in 2025-26, up from ₹4.6 lakh crore in the 99 bps in the preceding one-month period. corresponding period of the previous year. RBI Bulletin November 2025 53 52-naJ-01 52-naJ-13 52-beF-12 52-raM-41 52-rpA-40 52-rpA-52 52-yaM-61 52-nuJ-60 52-nuJ-72 52-luJ-81 52-guA-80 52-guA-92 52-peS-91 52-tcO-01 52-tcO-13 52-voN-12 7.5 7.0 6.5 6.0 5.5 5.52 5.0 52-naJ-01 52-naJ-13 52-beF-12 52-raM-41 52-rpA-4 52-rpA-52 52-yaM-61 52-nuJ-6 52-nuJ-72 52-luJ-81 52-guA-8 52-guA-92 52-peS-91 52-tcO-01 52-tcO-13 52-voN-12ARTICLE State of the Economy Chart IV.4: Upward Shift in Longer End of G-sec Yield Curve a. Movement in G-sec Yield b. G-sec Yield Curve (Per cent) (Per cent, left scale; basis points, right scale) 7.3 7.0 6.7 6.52 6.4 6.23 6.1 5.99 5.8 5.5 Tenor (years) Change (November 21, 2025 over October 23, 2025) [RHS] 23-Oct-2025 3 year 5 year 10 year 21-Nov-2025 Sources: Bloomberg; and RBI staff estimates. Money and Credit Credit growth of scheduled commercial banks During November so far (up to 14th), reserve (SCBs) picked up further in October. Deposit money growth declined, tracking the movements in growth, on the other hand, remained steady. currency in circulation.40 The moderation of growth With the pace of credit expansion outpacing deposit in currency in circulation was primarily due to the growth, the wedge between credit and deposit waning of festive demand. The growth in money growth widened to 160 bps from 90 bps in last month supply (M3) remained steady (Chart IV.5).41 (Chart IV.6).42,43 Table IV.1: Corporate Bonds - Yields and Spreads Instrument Interest Rates Spread (bps) (Per cent) (Over Corresponding Risk-free Rate) September 16, 2025 October 16, 2025 – Variation September 16, 2025 October 16, 2025 – Variation – October 15, 2025 November 20, 2025 – October 15, 2025 November 20, 2025 1 2 3 (4 = 3-2) 5 6 (7 = 6-5) (i) AAA (1-year) 6.69 6.72 3 104 108 4 (ii) AAA (3-year) 7.05 7.01 -4 108 103 -5 (iii) AAA (5-year) 7.21 7.20 -1 92 85 -7 (iv) AA (3-year) 8.18 8.08 -10 221 208 -13 (v) BBB- (3-year) 11.86 11.76 -10 590 572 -18 Note: Yields and spreads are computed as averages for the respective periods. Source: FIMMDA. 40 Reserve money (adjusted for the first-round impact of changes in the cash reserve ratio) grew by 7.8 per cent (y-o-y) as on November 14, 2025 [8.6 per cent (y-o-y) as on October 17, 2025]. Currency in circulation grew by 8.1 per cent (y-o-y) as on November 14, 2025 [8.8 per cent (y-o-y) as on October 17, 2025]. 41 Money supply grew by 9.3 per cent (y-o-y) as on October 31, 2025 [9.2 per cent (y-o-y) as on September 19, 2025]. 42 Credit growth of scheduled commercial banks was 11.3 per cent (y-o-y) as on October 31, 2025 [10.4 per cent (y-o-y) as on September 19, 2025]. Deposit growth was 9.7 per cent (y-o-y) as on October 31, 2025 [9.5 per cent (y-o-y) as on September 19, 2025]. The outstanding credit of scheduled commercial banks was at ₹193.9 lakh crore as on October 31, 2025 (₹189.0 lakh crore as on September 19, 2025) 43 The wedge was computed as the difference between credit and deposit growth rates on October 31, 2025, and September 19, 2025. 54 RBI Bulletin November 2025 52-naJ-01 52-naJ-13 52-beF-12 52-raM-41 52-rpA-40 52-rpA-52 52-yaM-61 52-nuJ-60 52-nuJ-72 52-luJ-81 52-guA-80 52-guA-92 52-peS-91 52-tcO-01 52-tcO-13 52-voN-12 7.5 7.35 15 7.24 7.0 10 6.5 5 6.0 0 5.5 5.0 -5 1 3 5 7 9 11 31 51 71 91State of the Economy ARTICLE During 2025-26 so far (up to October 31), total marked increase in the year so far (Table IV.2). As on flow of financial resources to commercial sector October 31, the total outstanding credit to commercial increased to ₹20.1 lakh crore from ₹16.2 lakh crore sector rose by 13.0 per cent from 12.0 per cent last a year ago. Non-bank sources − corporate bond year, with non-bank sources registering a growth of issuances, credit by non-banking financial companies 17.2 per cent compared to 12.4 per cent a year ago and foreign direct investment to India − showed a (Table IV.3).44 Chart IV.6: Scheduled Commercial Banks: Credit Expansion Surpassing Deposit Growth (Y-o-y, per cent) 12 11.3 10.4 10 9.5 9.7 8 Credit growth Deposit growth Note: Scheduled commercial banks’ data are inclusive of regional rural banks. Data include the impact of the merger of a non-bank with a bank. Source: Fortnightly Section 42 Returns, RBI. 44 For details, see Current Statistics Table No. 18(a) and 18(b). RBI Bulletin November 2025 55 52-naJ-01 52-naJ-42 52-beF-7 52-beF-12 52-raM-7 52-raM-12 52-rpA-4 52-rpA-81 52-yaM-2 52-yaM-61 52-yaM-03 52-nuJ-31 52-nuJ-72 52-luJ-11 52-luJ-52 52-guA-8 52-guA-22 52-peS-5 52-peS-91 52-tcO-3 52-tcO-71 52-tcO-13 Chart IV.5: Reserve Money Growth Moderated from Last Month amid Stable Money Supply (M3) Growth (Y-o-y, per cent) 12 11 9.3 10 9 8 7 6 7.8 5 4 Reservemoney (CRR adjusted) Money Supply Sources: RBI. 52-naJ-3 52-naJ-81 52-beF-2 52-beF-71 52-raM-4 52-raM-91 52-rpA-3 52-rpA-81 52-yaM-3 52-yaM-81 52-nuJ-2 52-nuJ-71 52-luJ-2 52-luJ-71 52-guA-1 52-guA-61 52-guA-13 52-peS-51 52-peS-03 52-tcO-51 52-tcO-03 52-voN-41ARTICLE State of the Economy Table IV.2: Flow of Financial Resources to Commercial Sector in India (₹ crore) Source April-March Up to October 31 2023-24 2024-25 2024-25 2025-26 P A. Non-Food Bank Credit 21,40,243 17,98,321 9,80,394 11,12,687 B. Non-Bank Sources (B1+B2) 12,63,721 17,10,459 6,43,105 8,95,813 B1. Domestic Sources 10,20,302 13,85,609 5,04,612 6,70,531 B2. Foreign Sources 2,43,419 3,24,850 1,38,493 2,25,282 C. Total Flow of Resources (A+B) 34,03,964 35,08,780 16,23,499 20,08,500 P: Provisional. Note: For detailed notes, please refer to Current Statistics Table No: 18(a). Sources: RBI; SEBI; and AIFIs; and RBI staff estimates. Across key sectors, bank credit continued to infrastructure and engineering segments observed exhibit steady growth in September, led by personal a rise in credit growth. The credit to the micro, loans, services, and industry (Chart IV.7).45,46 Personal small, and medium enterprises (MSMEs) segment accelerated further, and it continued to be the key loans sustained double-digit growth, with housing driver of industrial credit growth. The agriculture credit registering a modest increase, even as growth sector also witnessed an improvement in credit flow. in vehicle and gold loans moderated. Credit to the services sector witnessed a marginal deceleration in Deposit and Lending Rates growth. Non-banking financial companies (NBFCs) In response to the cumulative 100 basis points – the largest recipients of services sector credit reduction in the policy repo rate since February 2025, – and commercial real estate, however, recorded the one-year marginal cost of funds-based lending rate improvements. Within the industrial sector, of scheduled commercial banks declined by 45 basis Table IV.3: Outstanding Credit to Commercial Sector in India (₹ crore; Figures in parentheses are y-o-y percentage changes) Source At End-March As on October 31 2024 2025 2024 2025 P A. Non-Food Bank Credit 1,64,09,083 1,82,07,441 1,73,89,477 1,93,20,128 (20.2) (11.0) (11.7) (11.1) B. Non-Bank Sources (B1+B2) 77,56,314 88,85,434 81,00,470 94,90,483 (4.2) (14.6) (12.4) (17.2) B1. Domestic Sources 56,59,037 66,37,411 59,73,684 71,54,605 (4.9) (17.3) (15.8) (19.8) B2. Foreign Sources 20,97,277 22,48,023 21,26,786 23,35,877 (2.4) (7.2) (3.8) (9.8) C. Total Credit (A+B) 2,41,65,397 2,70,92,875 2,54,89,947 2,88,10,611 (14.5) (12.1) (12.0) (13.0) P: Provisional. Notes: For detailed notes, please refer to Current Statistics Table No: 18(b). Sources: RBI; SEBI; and AIFIs; and RBI staff estimates. 45 As at end-September, growth in non-food bank credit stood at 10.2 per cent (y-o-y). Non-food credit data are based on fortnightly Section-42 return for the last reporting Friday of the month, which covers all scheduled commercial banks. Sectoral non-food credit data are based on sector-wise and industry- wise bank credit (SIBC) return, which covers select banks accounting for about 95 per cent of total non-food credit extended by all scheduled commercial banks, pertaining to the last reporting Friday of the month. Data are provisional. The bank groups covered under the SIBC return are – Public Sector Banks, Private Sector Banks, Foreign Banks, and Small Finance Banks. Data includes the impact of the merger of a non-bank with a bank. 46 In terms of contribution to overall credit growth. 56 RBI Bulletin November 2025State of the Economy ARTICLE Chart IV.7: Acceleration in Credit Growth to Agriculture and Industry (Y-o-y, per cent) a. Credit: Agriculture b. Credit: Industry 20 15 10 9.0 5 c. Credit: Services d. Credit: Personal Loans Source: RBI. points during February-October 2025. The weighted rates on fresh term deposits. However, given the average lending rates on both fresh and outstanding relatively longer maturity profile of term deposits, rupee loans also registered a decline during February- the outstanding deposits have moderated, although September 2025. On the deposit side, banks reduced to a lesser extent (Table IV.4). Table IV.4: Monetary Transmission to Banks’ Deposit and Lending Rates (Variation in basis points) Term Deposit Rates Lending Rates Period Repo Rate WADTDR- WADTDR- EBLR 1-Year MCLR WALR - Fresh Rupee Loans WALR- Fresh Outstanding (Median) Outstanding Deposits Deposits Rupee Loans Overall Interest Rate Effect # (1) (2) (3) (4) (5) (6) (7) (8) (9) Tightening Period +250 259 206 250 175 182 191 115 May 2022 to Jan 2025 Easing Phase -100 -102 -27 -100 -45 -83 -73 -61 Feb 2025 to Sep* 2025 Note: 1. Data on EBLR pertain to 32 domestic banks. 2. *Data on MCLR as in October 2025. #: At constant weight. WALR: Weighted average lending rate; WADTDR; Weighted average domestic term deposit rate. MCLR: Marginal cost of funds-based lending rate; EBLR: External benchmark-based lending rate. RBI Bulletin November 2025 57 42-peS 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS 10 8 7.3 6 4 42-peS 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS 16 14 12 10 10.2 8 42-peS 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS 14 13 12 11.7 11 10 42-peS 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peSARTICLE State of the Economy The decline in the weighted average lending rate the current financial year, reflecting greater investor on fresh and outstanding rupee loans was higher participation. Consistent with this trend, both FPIs in the case of private banks relative to public sector and DIIs recorded positive cumulative flows in the banks (Chart IV.8). On the deposit side, transmission primary market (Chart IV.10a).47 In contrast, in the was higher for public sector banks compared to private banks. Chart IV.9: Revival in Domestic Equity Markets (Index, left scale; ₹ thousand crore, right scale) Equity Markets 89000 14 85231.92 12 Indian equity markets gained in October- 86000 10 November as optimism surrounding India-US trade 8 83000 deal and corporate earnings for Q2:2025-26 offset the 6 drag from uncertainty surrounding US-China trade 80000 4 negotiations. Domestic equity markets were also 2 77000 0 supported by a moderation in crude oil prices and a -2 policy rate cut by the US Federal Reserve. Realty, oil 74000 -4 and gas, and telecom emerged as the top-performing 71000 -6 sectors during October. In equity markets, domestic institutional investors (DIIs) continued to be net BSE Sensex FPI+DII flows (RHS) buyers, while foreign portfolio investors (FPIs) Source: Bloomberg; and Capitaline. turned net buyers in October after a phase of three consecutive months (Chart IV.9). 47 The primary market issuances (including Initial Public Offers, Follow- on Public Offers and Offer for Sale) increased from ₹13 thousand crore in Fund mobilisation through Initial Public Offers September 2025 to ₹45 thousand crore in October 2025, while the number of issuances moderated from 25 to 10 during this period (Source: Prime (IPOs) in the primary market remained strong in Database). 58 RBI Bulletin November 2025 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS 52-tcO 52-voN Chart IV.8: Transmission across Bank Groups (February 2025 – September 2025) a. Lending Rates b. Deposit Rates (Basis points) (Basis points) 0 0 -24 -24 -50 -50 -54 -68 -79 -100 -86 -87 -100 -91 -102 -96 -99 -123 -150 -150 WALR WALR WADTDR WADTDR (Fresh Rupee Loans) (Outstanding Rupee Loans) (Fresh Deposits) (Outstanding Deposits) Publicsectorbanks Private banks Foreign banks Publicsectorbanks Private banks Foreign banks Note: Transmission during February to September 2025 is calculated by subtracting the weighted average lending and deposit rates of January 2025 from those of September 2025. Source: RBI.State of the Economy ARTICLE secondary market, FPIs remained net sellers, while net basis.48 Gross inward FDI remained robust in DIIs continued to register strong net purchases September, with Singapore, Mauritius, the UAE, (Chart IV.10b). Luxembourg, and Qatar together accounting for about 78 per cent of total inflows (Chart IV.11a). The External Sources of Finance major recipient sectors were manufacturing, retail & During April-September 2025, FDI was higher wholesale trade, communication services, financial than the same period last year on both gross and services and computer services. However, net FDI Chart IV.11: Steady Gross Foreign Direct Investment Inflows a. Gross and Net FDI b. Country-Wise Outward FDI (US$ billion) (US$ billion) 15 10 6.6 5 0 -2.4 -5 -10 Net outward FDI Repatriation/Disinvestment Gross Inward FDI Net FDI Source: RBI. 48 Net FDI rose to US$ 7.6 billion from US$ 3.4 billion last year. Similarly, gross inward FDI at US$ 50.4 billion, registered a growth of 16.1 per cent (y-o-y). RBI Bulletin November 2025 59 42-peS 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS Chart IV.10: Rising Capital Flows to Primary Equity Markets a. Primary Markets b. Secondary Markets (Cumulative (cid:20)lows, ₹ thousand crore) (Cumulative (cid:20)lows, ₹ thousand crore) 40 35 30 25 20 15 10 5 0 DII Flows FPI Flows DII Flows FPI Flows Note: In chart a, data pertains to only mainboard IPOs. Sources: Prime Database, Capitaline, Bloomberg and RBI staff estimates. Singapore Mauritius UAE USA Luxembourg Netherlands UK 0.0 0.2 0.4 0.6 0.8 1.0 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS 52-tcO 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS 52-tcO 500 400 300 200 100 0 -100ARTICLE State of the Economy turned negative for the second consecutive month 2025. Despite this slowdown, inflows continued to due to a rise in outward FDI and repatriation.49 For outpace outflows resulting in net inflows of US$ 8.0 outward FDI, the key destinations were Singapore, billion (Chart IV.13). Notably, 45 per cent of the total Mauritius, the UAE and the US, while major sectors ECBs registered during this period were raised for included financial services, insurance & business capital expenditure. services, agriculture & mining and manufacturing Amidst global trade policy uncertainties and (Chart IV.11b). external sector headwinds, India’s economy is During 2025–26 so far (up to November 20), turning out to be more resilient to external shocks net FPI registered inflows driven largely by the debt over time, backed by strong services exports, segment while equity registered net outflows.50 The remittances receipts and oil prices becoming less debt segment continued to witness net inflows, detrimental to the current account sustainability. The supported by expectations of a US Fed rate cut and increasing share of renewables in India’s energy mix favourable yield differentials. In October, net FPI is adding further resilience. Key external vulnerability flows turned positive after three consecutive months indicators improved as of end-June from their levels of outflows, supported by robust quarterly earnings, at end-March 2025 (Table IV.5). The current account improved valuations, and IPO issuances (Chart IV.12). deficit to GDP ratio remained modest in Q1:2025- In November so far (up to 20th), net FPI flows remained 26. The external debt to GDP ratio also stayed at a positive supported by both equity and debt segment. comfortable level and the ratio of short-term debt to The registrations of external commercial total external debt, and to total reserves remained borrowings (ECBs) moderated during April - September low.51 In addition, the ratio of volatile capital flows Chart IV.12: Foreign Portfolio Investors Turned Net Buyers in October (US$ billion) 15 10 5 3.6 0 -5 -10 -15 Equity Debt Total Notes: 1. Debt also includes investments under the hybrid instruments. 2. *: Data up to November 20. Source: National Securities Depository Limited (NSDL). 49 Net outward FDI, on a year-on-year basis, increased by 64.5 per cent in September 2025 as against 78.4 per cent in September 2024. 50 Net FPI inflows amounted to US$ 0.4 billion during 2025–26 so far (up to November 20). 51 The ratio of short-term debt (residual maturity) to total external debt and to total reserves stood at 40.7 per cent and 43.6 percent, respectively, as of end-June 2025. 60 RBI Bulletin November 2025 42-rpA 42-yaM 42-nuJ 42-luJ 42-guA 42-peS 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS 52-tcO *52-voN Chart IV.13: External Commercial Borrowings - Registrations and Flows Moderated (US$ billion) 30 25 20 18.5 15 10 8.0 5 2.80 2.2 0 -5 -10 Registrations Net inflows Source: Form ECB, RBI. 42-rpA 42-yaM 42-nuJ 42-luJ 42-guA 42-peS 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS 4202 peS-rpA 5202 peS-rpAState of the Economy ARTICLE Table IV.5: External Vulnerability Indicators Item End-March 2013 End-March 2024 End-March 2025 End-June 2025 (Pre-Taper Talk) Current account balance (per cent of GDP) -4.8 -0.7 -0.6 -0.2 External Debt (per cent of GDP) 22.4 18.5 19.1 18.9 Short-term Debt (per cent of Reserves) 59.0 44.9 45.4 43.6 International Investment Position (per cent of GDP) -17.8 -10.1 -8.6 -8.0 Reserve cover for imports (in months) 7.0 11.3 11.0 11.4 Reserve to External Debt (per cent) 71.3 96.7 90.8 93.4 Volatile capital flow (per cent of Reserves) 96.1 69.8 69.0 66.6 Note: The import cover data is based on annualised merchandise imports as per the balance of payments statistics. Source: RBI. (comprising cumulative portfolio inflows and of a stronger US dollar following the US Fed’s policy outstanding short-term debt) to foreign exchange announcement around the end of the month. In mid- reserves has also declined.52 India’s foreign exchange October, however, the INR registered a brief but sharp appreciation, supported by optimism over India-US reserves remain adequate to cushion the impact trade talks and renewed net FPI inflows (Chart IV.15). of any external shocks to the balance of payments Consequently, rupee volatility increased marginally (Chart IV.14).53 during the month, although it remained relatively Foreign Exchange Market contained compared with most major currencies. The Indian rupee (INR) depreciated slightly In November so far (up to 21st), the INR appreciated against the US dollar in October, reflecting the impact slightly by 0.1 per cent over its end-October level. Chart IV.14: India’s Foreign Exchange Reserves Adequate (US$ billion, left scale; months, right scale) 750 14 693 12 650 11.3 10 8 550 6 4 450 2 350 0 Foreign exchange reserves Import cover (RHS) Notes: 1. *: As on November 14, 2025. 2. The import cover data is based on annualised merchandise imports as per the balance of payments statistics. Source: RBI. 52 Ratio of volatile capital flows to foreign exchange reserves declined from 69.0 per cent at end-March 2025 to 66.6 per cent at end-June 2025. 53 India’s foreign exchange reserves provide a cover for more than 11 months of goods imports, 9 months of import of goods and services combined, and around 93 per cent of the external debt outstanding as at end-June 2025. RBI Bulletin November 2025 61 32-raM 32-nuJ 32-peS 32-ceD 42-raM 42-nuJ 42-peS 42-ceD 52-raM 52-nuJ 52-tpeS 52-tcO *52-voN Chart IV.15: Movement of Major Currencies against the US Dollar in October (Per cent, m-o-m, left scale; per cent, right scale) 1.5 2 1.0 0.5 1 0.0 -0.5 0 -1.0 -1.5 -1 -2.0 -2.5 -2 Percentage change (+ appreciation/ - depreciation) Volatility (RHS) Notes: 1. Appreciation/depreciation (m-o-m) calculated using monthly average exchange rates. 2. US dollar (DXY) measures the movements of the US dollar against a basket of major currencies (Euro, Japanese yen, British pound, Canadian dollar, Swedish krona and Swiss franc). 3. For each currency, volatility is measured as the coefficient of variation (100*Standard Deviation/Mean) using daily exchange rate data for October 2025. Sources: FBIL; Thomson Reuters; and RBI staff estimates. )YXD( ralloD SU xednI ycnerruC EME dnar nacirfA htuoS osep nacixeM gnod esemanteiV rallod gnoK gnoH nauy esenihC eeeeppuurr nnaaiiddnnII tiggnir naisyalaM laer nailizarB haipur naisenodnI oruE dnuop KU thab dnaliahT osep enippilihP now naeroK ney esenapaJARTICLE State of the Economy Chart IV.16: Movements in the 40-Currency Real Effective Exchange Rate a. Monthly Changes b. Decomposition of Monthly Changes (Index (2015-16 = 100), left scale; per cent, right scale) (Per cent) 110 4 108 106 2 104 102 0.1 100 0 98 96 97.5 -2 94 92 90 -4 Relative price effect Change in REER Change in REER(RHS) REER Nominal exchange rate effect Note: Positive change indicates an appreciation of the nominal and real exchange rate and negative change indicates a depreciation. Source: RBI. In real effective terms, the Indian rupee The World Bank’s Financial Sector Assessment appreciated marginally in October mainly driven (FSA) report of October 2025 highlighted a financial by appreciation in nominal effective exchange rate system growing in resilience and strength. Improved (Chart IV.16). macroeconomic frameworks and outcomes have not V. Conclusion only enhanced the ability of financial institutions to support the macroeconomy but also allowed the The month of October has seen a further pick up Reserve Bank to better calibrate regulatory measures, in demand conditions pointing towards a resilient to improve the efficiency of financial intermediation growth outlook. Headline inflation has fallen to and augment the flow of credit to the broader a historic low in October, significantly helped by economy. The fiscal, monetary, and regulatory favourable supply-side factors, including the prospects of a good kharif season and the reduction of GST measures undertaken so far this year should pave the rates. The external sector’s capacity to absorb shocks way for a virtuous cycle of higher private investment, has also improved over time, building resilience amid productivity, and growth, leading to long-term global trade policy uncertainties. economic resilience. 62 RBI Bulletin November 2025 42-rpA 42-yaM 42-nuJ 42-luJ 42-guA 42-peS 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS 52-tcO 4 2 0.05 0 0.07 0.03 -2 -4 42-rpA 42-yaM 42-nuJ 42-luJ 42-guA 42-peS 42-tcO 42-voN 42-ceD 52-naJ 52-beF 52-raM 52-rpA 52-yaM 52-nuJ 52-luJ 52-guA 52-peS 52-tcO‘Making the Horizons Meet’: A Heterodox Approach for ARTICLE Short-Term Inflation Forecasting ‘Making the Horizons Meet’: to structural ones like dynamic stochastic general equilibrium (DSGE) models. In the shorter-end of the A Heterodox Approach for forecast horizon, data dependent models outperform Short-Term Inflation Forecasting the structural models, however they are not suited for policy analysis (Lucas, 1976). Structural models by Joice John, Saquib Hasan, like DSGE are good at policy analysis but may not be favoured in terms of forecast accuracy, especially Renjith Mohan, and Suvendu Sarkar^ in the near-term (Del Negro and Schorfheide, 2013). At the near-end of the spectrum of any forecasting This article presents a framework that generates framework, there are observed data or nowcasts short-term inflation forecasts integrating three diverse (since, in the near term, several auxiliary information procedures: (i) nowcasts (ii) machine learning and are available) as the initial condition. However, as the statistical methods and (iii) system of dynamic and horizon extends, precise information/data becomes stochastic equations, allowing nowcasts in the near-end scarcer. Hence, the forecasts become more dependent of the horizon to converge to the benchmark forecasts, on structural characteristics like persistence, accentuated or delayed by persistence, spillovers, and expectations, spillovers, and macro-linkages1. macro-linkages. The framework is built on seasonally- The above mentioned characteristics underscore adjusted disaggregated monthly data of 33 sub-groups/ the need for a forecasting framework, which components of the CPI-Combined. It employs techniques identifies the path of convergence from observed such as full information matrix, machine learning and data or nowcasts (in the near horizon) to a dynamic statistical models, Bayesian estimation, Kalman filtering equilibrium2/benchmark forecast that is generated and dynamic optimisation to produce point as well as using an atheoretical framework. There can be density forecasts of inflation. multiple paths through which nowcast can converge Introduction to the benchmark forecasts (Chart 1). By conducting monetary policy, central banks Using dynamic optimisation, the proposed short- play a vital role in guiding economies towards term forecasting model (STFM) identifies the path macroeconomic stability and growth. While setting that allows convergence from nowcast to benchmark those policies, due to the lags in transmission and forecast, accentuated or delayed by persistence, other nominal rigidities, monetary policy often spillovers from different inflation components and focusses on forecasts of the macro variables as linkages from other macro variables like output gap, intermediate targets. In this context, consistent and exchange rate and cost conditions. This framework reliable forecasts become vital for the conduct of also gives the flexibility to incorporate judgmental monetary policy. More specifically, inflation forecasts adjustments to this convergence process based on are central to the monetary policy conducted by views from sectoral developments. Thus, the short- inflation targeting (IT) central banks. For forecasting term forecasting model acts as a bridge navigating inflation, there are diverse approaches available from nowcasts in the near-horizon to benchmark in the literature, from data dependent ones, like 1 In recent times, high frequency models of macro-linkages are gaining statistical, econometric and machine learning models prominence e.g., Bayesian Machine Learning models and Gaussian Process and Bayesian Additive Regression Tree (BART). However, these models ^ The authors are from the Department of Statistics and Information also, are dependent on data. Management, Reserve Bank of India. The views expressed in this article 2 Dynamic equilibrium is a state where a system is balanced, the are those of the authors and do not represent the views of the Reserve macroscopic properties remain stable, even though changes are occurring Bank of India. at microscopic level. RBI Bulletin November 2025 63ARTICLE ‘Making the Horizons Meet’: A Heterodox Approach for Short-Term Inflation Forecasting II. Short-term Inflation Forecasting Tool: An Eagle- Chart 1: From Nowcasts to Benchmark Forecast: Multiple Paths Eye View of System Architecture and Framework (m-o-m, per cent) Design This section presents a high-level overview of the short-term inflation forecasting framework. It is engineered around a layered architecture that integrates diverse information sources, dynamic interlinkages and stochastic processes that allow nowcasts to converge to the benchmark forecast. The system is organized into three layers: the Input Layer (Step 1), the Model Layer (Step 2), and the Output Note: 1. This is an illustative chart and do not use any actual data, hence no Layer (Step 3) (Chart 2). axis values are mentioned. 2. Black line: Actual; Dotted Black line: Benchmark forecasts (Dynamic equilibrium); Red triangle: Nowcasts; and Dotted redlines: Possible II.1. Input Layer (Initial Conditions): The Input convergence paths. Source: Authors’ Illustration. Layer constitutes the historical data (CPI as well as other macro variables), seasonal factors, nowcasts and forecasts in a short-to-medium-horizon. This hybrid benchmark forecasts. architecture offers a more pragmatic and policy relevant forecasting solution. This article delves into a) Historical CPI Momentums: The input layer the nitty-gritty of such a framework designed for incorporates the historical data on monthly basis short-term inflation forecasting in the Indian context. for the 33 CPI sub-groups and component series3 Chart 2: A High-level Architecture of the Short-term Inflation Forecasting Model Source: Authors’ Illustration. 3 Along with the officially classified 22 CPI sub-groups, 11 ‘fuel’ items are also considered in the model framework separately. Thus, the 33 sub-groups/ components are ‘Cereals & products’, ‘Pulses & products’, ‘Milk & products’, ‘Eggs’, ‘Meat & Fish’, ‘Vegetables’, ‘Fruits’, ‘Spices’, ‘Oil & Fats’, ‘Sugar & confectionary’, ‘Non-Alcoholic Beverages’, ‘Prepared meals’, ‘Electricity’, ‘LPG’, ‘Kerosene-PDS’, ‘Kerosene-Other’, ‘Diesel’, ‘Other fuel’, ‘Coke’, ‘Firewood & chips’, ‘Coal’, ‘Charcoal’, ‘Dung cake’, ‘Housing’, ‘Pan, Tobacco & Intoxicants’, ‘Clothing’, ‘Footwear’, ‘Household’, ‘Health’, ‘Transport & communication’, ‘Recreation & amusement’, ‘Education’, and ‘Personal care & effects’. 64 RBI Bulletin November 2025 01-t 9-t 8-t 7-t 6-t 5-t 4-t 3-t 2-t 1-t 0-t 1-t 0+t 1+t 2+t 3+t 4+t 5+t 6+t 7+t 8+t 9+t 01+t 11+t Months‘Making the Horizons Meet’: A Heterodox Approach for ARTICLE Short-Term Inflation Forecasting for the period from February 2011 onwards. This Government of India (GoI)), daily wholesale and database forms the basis for the structure and retail prices (Department of Consumer Affairs, parametrisation of the model, as well as the initial GoI), in-house price surveys and qualitative inputs condition for the short-term forecast in absence (e.g., media intelligence, supply-side government of any nowcast information. measures etc.). This matrix acts as a real-time intelligence dashboard, capturing the most recent b) Other Macro Variables: A set of macroeconomic developments in price behaviour across these variables, which includes output gap, exchange components. These nowcasts reflect the near- rate, commodity prices and domestic fuel costs term momentum in prices and act as the initial act as the conduits of macro-linkages to headline conditions for the short-term forecasting model. inflation, affecting through different sub-groups/ components. These inputs are integrated within e) Benchmark Forecasts (model dependent a semi-structural model to account for exchange forecasts): Benchmark forecasts are generated rate passthrough, imported inflation, cost push using a performance-weighted forecast pressures and demand-side effects on inflation. combination approach (Mohan et al., 2025; John et al., 2020) using seasonally adjusted momentums c) Seasonal Factors: The model parameterisation for each of the 33 CPI sub-groups/components. and forecasts are carried out using the seasonally For each component, this approach combines adjusted data. The seasonal adjustment process the forecasts from a suite of statistical, machine has been carried out on the month-on-month learning (ML) and deep learning (DL) models (m-o-m) changes of each of the 33 CPI series containing univariate and multivariate models, separately. These are carried out using the X-13 which includes autoregressive integrated moving ARIMA-SEATS seasonal adjustment procedure4 average (ARIMA), vector autoregression (VAR), using the data from February 2011 onwards, with Bayesian VAR (BVAR), support vector machine additive restrictions. Further, the average monthly (SVM), random forests, nonlinear autoregressive seasonal factors are also computed separately for neural networks (NARNET) and long short- each of the 33 CPI sub-groups/components. These term memory (LSTM) models with different average seasonal factors are used in the later specifications. Thus, generating 216 forecasts stage along with the seasonally adjusted m-o-m for each of the 33 CPI sub-groups/components. forecasts of 33 CPI sub-groups/components for Forecasts for individual models are then generating the headline inflation forecast. combined by weights generated using inverse of d) Nowcasts (data dependent forecasts): The pseudo-out-of-sample5 root mean squared errors nowcast in this forecasting framework serves as (RMSEs), separately for each of these 33 sub- the initial condition across the 33 CPI sub-groups/ groups/components (Chart 3). components, separately. The nowcasting process The benchmarks forecasts are atheoretical by is derived from a comprehensive full information design, making them an ideal reference point for matrix constructed using all available early price nowcast to converge to. They reflect the information signals—both quantitative (e.g., daily mandi contained in historical patterns and derived out prices (Agmarknet, Ministry of Agriculture, 5 In a pseudo out-of-sample forecasting exercise, the forecasts are 4 X-13ARIMA-SEATS is seasonal adjustment software produced, generated at time t-h (for h = 1 to m) in the past, using only the data distributed, and maintained by the US Census Bureau (For reference available till that time (t-h) for parametrisation of the model as well as for material see Monsell, Lytras and Findley, 2016). generating the forecast of the exogenous and endogenous variables. RBI Bulletin November 2025 65ARTICLE ‘Making the Horizons Meet’: A Heterodox Approach for Short-Term Inflation Forecasting Chart 3: Benchmark Forecasting Model Source: Authors’ Illustration. of model-imposed dynamics. This makes them (say passthrough effects from fuel prices and cost- particularly important within the broader forecasting push pressures), macro-linkages (say exchange rate framework – they act as the dynamic equilibrium/ passthrough to inflation and demand sensitivity) benchmark. and idiosyncratic shocks. For each sub-group, inflation is governed by a system of identities and II.2. Model Layer (Short-term Forecasting Model): behavioural equations, such as identities linking The Model Layer is the analytical engine of our short- the seasonally adjusted momentum and seasonal term forecasting framework. It is a semi-structural factors; closing identities for seasonal factors and model incorporating persistence, spillovers, and benchmark forecasts; and dynamic behavioural macro-linkages. It transforms the inputs into forecasts equations capturing the evolution of seasonally through Bayesian posterior updation, Kalman filtering adjusted momentums. The behavioural equations and dynamic optimisation. This layer is bifurcated are specified as a function of lagged inflations into two subsystems: Model Structure, and Model (capturing intrinsic persistence), exogenous macro Parametrisation and Estimation. Model structure in drivers (e.g., output gap, cost-push pressures sub-section (a) describes the equations in STFM, which and exchange rate movements), spillover effects are characterised by persistence, macro-linkages and from other sub-groups/components, benchmark interlinkages among the various CPI sub-groups/ forecasts, and stochastic shocks. The set of components. Model Parametrisation and Estimation equations are as below: in sub-section (b) describes the parameter estimation process. For all elements in a) Model Structure: Inflation dynamics is modelled Var = {‘Cereals & products’, ‘Pulses & products’, with a set of transition equations specified for ‘Milk & products’, ‘Eggs’, ‘Meat & Fish’, each of the 33 CPI sub-groups/components, ‘Vegetables’, ‘Fruits’, ‘Spices’, ‘Oil & Fats’, ‘Sugar allowing each category to respond to its own & confectionary’, ‘Non-Alcoholic Beverages’, persistence, spillovers from other components ‘Prepared meals’, ‘Electricity’, ‘LPG’, ‘Kerosene- 66 RBI Bulletin November 2025‘Making the Horizons Meet’: A Heterodox Approach for ARTICLE Short-Term Inflation Forecasting PDS’, ‘Kerosene-Other’, ‘Diesel’, ‘Other fuel’, Table 1: Dimension of the Short-term Forecasting ‘Coke’, ‘Firewood & chips’, ‘Coal’, ‘Charcoal’, ‘Dung Model cake’, ‘Housing’, ‘Pan, Tobacco & Intoxicants’, Number of CPI sub-groups/components 33 ‘Clothing’, ‘Footwear’, ‘Household’, ‘Health’, Number of equations 134 ‘Transport & communication’, ‘Recreation & Number of variables 134 amusement’, ‘Education’, ‘Personal care & Number of shocks 101 Number of parameters 89 effects’} Number of measurement equations 68 (1) Number of observed variables 68 Source: Authors’ Estimates. = + (2) b) Model Parametrisation and Estimation: The = {}+ {} (3) model parameters are estimated using Bayesian {} = {} techniques. The unobserved variables are filtered out using Multivariate Kalman Filter. Using the estimated posterior parameters and initial conditions, as provided by nowcasts, the h-period (4) ahead forecasts are then generated using dynamic is the m-o-m per cent change in the ith optimisation. variable in Var. {} The Bayesian estimation is carried out using is the seasonally adjusted m-o-m per the Metropolis-Hastings6-Markov Chain Monte cent c{h}ange in the ith variable in Var. Carlo7 (MH-MCMC) method. For each parameter in the model, a prior distribution is specified as is the seasonal factors of the m-o-m per lognormal distribution centred around a prior cent c{h}ange in the ith variable in Var. mode, which are identified using single equation is the benchmark forecasts of the econometric methods. The MH algorithm season{a}lly adjusted m-o-m per cent change in the iteratively draws from the proposed distribution ith variable in Var. and accepts or rejects samples based on the OG and Ex are output gap and exchange rate, posterior likelihood, eventually converging to respectively. the target posterior modes as defined by Bayes’ rule. The estimation is governed by a set of Equation (1) represents the identity linking seasonally adjusted and unadjusted series. convergence criteria, including tolerances on Equations (2) and (3) are used for closing the function values, subject to constraints and model structure. The benchmark forecasts bounded by a maximum number of iterations. ( ) and seasonal factors ( ) in the Once the posterior sampling is completed, the entire f {o }recast horizon are provided as {ex }ogenous 6 MH algorithm (Metropolis et al., 1953; Hastings, 1970) is the most inputs. Equation (4) represents the behavioural popular technique to build Markov chains (series of dependent samples) with a given invariant distribution. While Metropolis et al. (1953) requires equation encompassing persistence, spillovers, that the proposed distribution be symmetric, Hastings (1970) generalises and macro impacts, which allows the convergence it to allow asymmetric distributions. 7 MCMC methods generate Markov chains, which over time converges to from nowcasts (initial condition) to the a desired stationary distribution. This method is used for approximating benchmark forecasts. The dimension of the short- complex distributions and estimating its parameters, even when theoretical closed-form solutions are unavailable. For details, refer to term forecasting model is presented in Table 1. Brooks (1998). RBI Bulletin November 2025 67ARTICLE ‘Making the Horizons Meet’: A Heterodox Approach for Short-Term Inflation Forecasting posterior modes are computed from the MCMC (SD), which in turn is applied on the point draws and stored for subsequent use in filtering forecasts to obtain the density forecasts, assuming and forecast generation. An adaptive random- a normal distribution13. walk Metropolis (ARWM) posterior simulator8 is II.3. Output Layer: The Output Layer forms the used to draw samples from the prior distribution final stage of the forecasting system, transforming and uses estimated posterior modes to generates the forecasted momentum (expressed in m-o-m per a large chain of iterations (here 5,00,000) to cent change) paths of each of the 33 CPI sub-groups/ reach stationary posterior distributions. These components—generated in the Model Layer—into distributions are used to generate 95 per cent forecasts of indices, year-on-year (y-o-y) inflations and credible intervals (CI)9. Further, the unobserved contributions. variables are filtered out using multivariate Kalman smoothing procedure10. Then, through The momentum forecasts are applied to the one- a dynamic optimisation process11, the estimated period prior observed/estimated indices to recursively system guides nowcasts towards the benchmark construct the forecasted indices for each component/ forecasts, which provide the point forecasts for sub-group. These are then aggregated into broader each of the components and groups. Further, categories14,—’Food & Beverages’, ‘Fuel & Light’, and density forecasts for each of the variables ‘Core’ (Ex-Food & Fuel)—using CPI weights. ‘Fuel & are generated using multivariate and time- Light’ sub-group, provides an additional challenge due simultaneous prediction bands12. Here, the to the aggregation biases15. Hence, for ‘Fuel & Light, forecast mean square error matrices are used to an additional refinement is introduced. The weighted generate the forecast error standard deviations statistical moments (variance, skewness, and kurtosis) 8 In ARWM, a proposed distribution (here Normal) is updated adaptively of the constituent fuel items are used as predictors for using the full information accumulated so far. Due to its adaptive nature estimating the aggregation biases. From the forecasted the ARWM algorithm is non-Markovian, however it has the right ergodic properties. ARWM, thus overcomes the issue of the choice of a proper indices, the y-o-y inflation and m-o-m rates for each distribution, which is vital for the convergence in the traditional MCMC sub-groups/components and at aggregated (groups algorithms (Haario et al., 2001). 9 Credible intervals are intervals generated from the posterior probability and headline) levels are then calculated. density function. It can be interpreted similar to the confidence interval in the frequentist approach. For e.g., a 95% credible interval is having 95 A toolbox has been developed in Matlab®, per cent probability that the true value of the estimate would lie within that interval. using the IRIS16 and MikTex©17 to support the model 10 Multivariate Kalman filter uses observed variables and stochastics estimation, forecasting and output generation – noises over time to filter out unknown variables, using a multivariate state-space model, which applies the joint probability distributions in including forecast tables and charts – compiled into a each time-step. This system level approach tends to be more accurate than those based on a single measurement variable and a single equation. A publication-ready report. Kalman smoothing process uses both past and future values and tend to be even more accurate. For details, refer to Barratt and Boyd (2020). 13 The framework can also be used to generate asymmetric confidence 11 Dynamic optimisation involves the following steps: 1) steady state interval forecasts using a split-normal distribution. solutions are obtained using Newton-type algorithm, 2) dynamic solutions, 14 The 33 component/sub-group level indices are aggregated into which guides the disequilibria at any time to the steady state, are obtained three respective groups (Food, Fuel and Core) using the CPI-C weights. using particle swarm optimizer (Eberhart and Kennedy, 1995). 3) The Subsequently, headline index is calculated using the group-wise CPI-C point forecasts are then generated using equation-selective simulator with weights. Shanks acceleration (a non-linear algorithm which improves the rate of 15 The weighted vertical aggregation of the item-level indices does not convergence). match with the published ‘Fuel & Light’ index (Das and George, 2023). 12 Multivariate and time-simultaneous prediction bands aim to capture 16 IRIS is an open-source toolbox for macroeconomic modelling and possible outcomes for all variables at all horizons within any specified forecasting in Matlab®, originally developed by the ‘IRIS Solutions Team’ confidence level. This is used to forecasts confidence bands of different and currently maintained and supported by the ‘Global Projection Model related time series by simultaneously considering the temporal Network’. https://iris.igpmn.org/ uncertainty as well as their interlinkages across different variables. These are generated by estimating forecast mean square error matrices (Kolsrud, 17 MikTex© is an open-source TeX /LaTeX editor for creating, typesetting, 2007). and previewing documents. 68 RBI Bulletin November 2025‘Making the Horizons Meet’: A Heterodox Approach for ARTICLE Short-Term Inflation Forecasting III. Results The estimated parameters and 95 per cent CI are provided in Table 2. Table 2: Bayesian Estimated Parameters S. CPI Sub-Group Para- Prior Posterior 95% CI Sr. CPI Sub-Group Para- Prior Posterior 95% CI No meters Mode Mode No meters Mode Mode Lower Upper Lower Upper a 0.53 0.70 0.47 0.95 a 0.00 0.71 0.46 0.95 19 Coke 1 Cereals c1 1 0.00 0.01 0.00 0.58 b1 19 9 0.00 0.00 0.01 0.82 a1 0.00 0.21 0.01 0.51 a19 0.12 0.76 0.51 0.92 20 Coal 2 a1 17 0.65 0.76 0.31 0.95 b2 20 0 0.53 0.05 0.01 0.89 Pulses c2 2 0.10 0.02 0.01 0.84 a20 0.44 0.35 0.07 0.76 a2 0.00 0.15 0.01 0.56 a2 21 1 0.35 0.23 0.02 0.59 3 Meat & Fish a1 27 0.43 0.78 0.32 0.95 21 Firewood a1 24 1 0.00 0.32 0.01 0.59 a3 3 0.01 0.05 0.00 0.10 a1 29 1 0.00 0.07 0.00 0.31 4 Egg a c1 4 437 0 0. .0 23 1 0 0. .8 00 0 0 0. .5 06 0 0 0. .9 87 1 22 Charcoal a a1 2 2 25 1 2 2 0 0. .0 16 5 0 0. .7 77 0 0 0. .5 42 6 0 0. .9 95 5 5 6 M Oii ll k & Fats a a a bc4 55 5 1 5 6 6 0 0 0 0 0. . . . .0 1 0 2 30 7 0 2 4 0 0 0 0 0. . . . .6 0 3 8 00 0 6 5 1 0 0 0 0 0. . . . .0 0 1 6 06 0 9 3 0 0 0 0 0 0. . . . .8 6 9 9 83 3 5 7 8 2 23 4 D Hu oun sg i nca gke a a a c2 2 1 2 1 2 2 23 3 7 3 5 3 4 4 0 0 00. . ..0 0 063 9 18 0 0 00. . ..0 0 087 9 04 0 0 00. . ..0 0 050 0 04 0 0 00. . ..1 1 790 0 46 7 Fruits aa6 7 7 0 0. .0 70 3 0 0. .8 02 2 0 0. .4 08 0 0 0. .9 08 5 a a2 1 24 3 4 0 0. .0 01 0 0 0. .0 04 1 0 0. .0 00 0 0 0. .1 00 5 8 Vegetables a1 77 0.00 0.53 0.29 0.76 25 Pan, Tobacco & a1 27 4 0.06 0.83 0.59 0.98 9 a8 8 0.67 0.85 0.48 0.97 Intoxicants c2 25 5 0.02 0.01 0.00 0.62 Spices a9 9 0.05 0.04 0.00 0.10 a25 0.41 0.74 0.36 0.96 a1 97 0.36 0.66 0.16 0.83 26 Clothing c2 26 6 0.01 0.02 0.01 0.79 10 Sugar c1 10 0 0.01 0.02 0.01 0.68 d26 0.00 0.17 0.01 0.47 aa1 1 10 7 0 0 0. .0 61 7 0 0. .2 65 9 0 0. .0 22 1 0 0. .6 84 5 27 Footwear a c2 2 26 7 7 0 0. .2 09 1 0 0. .8 03 0 0 0. .5 08 1 0 0. .9 67 7 11 Beverages a a a1 1 1 1 1 11 1 7 1 3 1 0 0 0. . .0 0 00 5 0 0 0 0. . .0 0 11 6 8 0 0 0. . .0 0 00 0 5 0 0 0. . .1 1 70 0 0 28 H Goo ou dse s h &o Sld e rvices a a c2 1 2 2 27 7 7 8 8 0 0 0. . .0 0 00 0 2 0 0 0. . .0 8 01 3 1 0 0 0. . .0 5 00 7 0 0 0 0. . .0 9 65 7 5 a ac 11 1 1 1 20 1 2 2 0 0 0. . .3 0 04 2 0 0 0 0. . .3 0 16 1 1 0 0 0. . .0 0 00 0 1 0 0 0. . .5 7 50 7 5 29 Health ab c2 2 2 28 8 9 9 0 0 0. . .0 0 03 0 1 0 0 0. . .0 7 03 9 0 0 0 0. . .0 4 01 8 0 0 0 0. . .8 9 77 6 0 12 P mr ee ap la sred a a a a a a1 1 1 1 1 1 2 1 1 11 52 2 5 2 7 2 1 2 0 2 00 0 0 0 0 .. . . . . 00 0 0 1 0 80 8 0 7 0 00 0 0 0 0 .. . . . . 31 0 0 2 1 32 6 3 0 1 00 0 0 0 0 .. . . . . 00 0 0 0 0 40 1 0 0 0 00 0 0 0 0 .. . . . . 74 4 2 3 5 50 3 8 6 5 3 30 1 T C R Ar eo ma cmn r ues m sap etuo mionr et ni nc& a & t t ions a a aa ab2 31 2 3 3 1 3 1 3 39 03 9 0 0 4 0 7 0 1 0 0 0 0 0 0. . . . . .0 2 0 0 0 00 8 6 0 6 0 0 0 0 0 0 0. . . . . .0 7 0 0 0 85 8 6 1 7 2 0 0 0 0 0 0. . . . . .0 4 0 0 0 40 6 0 0 1 9 0 0 0 0 0 0. . . . . .1 9 8 0 0 90 7 8 5 7 7 13 a1 13 3 0.02 0.13 0.01 0.43 c31 0.01 0.00 0.00 0.54 Electricity a1 17 3 0.09 0.19 0.01 0.78 a31 0.00 0.76 0.47 0.95 a1 19 3 0.00 0.31 0.00 0.48 32 Education c3 32 2 0.03 0.01 0.00 0.66 14 LPG a2 10 3 0.32 0.74 0.48 0.95 a32 0.05 0.09 0.00 0.10 b1 14 4 0.05 0.01 0.00 0.74 a1 33 2 0.26 0.74 0.55 0.97 Personal care & 15 Kerosene-PDS a14 0.01 0.42 0.18 0.64 33 effects c3 33 3 0.29 0.31 0.02 0.94 16 Kerosene- a1 15 5 0.19 0.81 0.53 0.98 a33 0.04 0.15 0.01 0.20 Other b1 16 6 0.05 0.05 0.01 0.93 Note: ai j measures impact of 3i6 t3h CPI sub-group on jth sub-group momentum, for 17 Diesel a b1 1 16 7 7 0 0. .1 19 0 0 0. .7 12 2 0 0. .4 02 2 0 0. .9 92 0 tti= hh eej , i e mt xh c pe h a p a cna t r g oa e fm orae utt tee p r up m a t s ge s aa pts hu orr noe u s t g ht hh e e o j thnp s e utr hs bei -s g t j rte ohn usc pue b mo -gf or o mjth u epC n P tm uI o msu mb eg nro tuu mp, , b cj j mm ee aa ss uu rr ee ss 18 Other Fuel a17 0.00 0.78 0.35 0.92 Source: Authors’ Estimates. 18 18 RBI Bulletin November 2025 69ARTICLE ‘Making the Horizons Meet’: A Heterodox Approach for Short-Term Inflation Forecasting The impact of some key macro variables on nowcast as the initial condition, the short-term headline y-o-y inflation can be derived from this forecasting model allow nowcast to converge to framework (Chart 4). This includes both direct and the benchmark forecast (red dotted line, Chart 6). indirect effects. Here, it could be interpreted that in case of this sub-component, the difference in the near-term Ten per cent depreciation in exchange rate leads is largely transitory. However, the persistence, to around 80 basis points (bps) increase in inflation spillovers, and macro-linkages has slowed down the over a period of 12 months (Chart 4.a). If demand convergence of momentum forecast to benchmark conditions increase by 1 per centage point (ppt), headline inflation increases by close to 20 bps over Chart 5: Forecast (m-o-m, per cent ) a period of one year (Chart 4.b). The ten per cent 1 increase in diesel (pump) prices, will lead to an increase in the headline inflation by around 90 bps over a 12-month horizon (Chart 4.c). 0 Using one of the components as an example, Chart 5 illustrates the convergence of nowcast at period (t+0) to the benchmark forecast at horizon (t+11), generated from the short-term forecasting -1 framework. This chart indicates that the nowcast (red triangle marker, Chart 5) of this component Actual Nowcasts estimated from the high-frequency data is largely Benchmark Forecasts Short-term Forecasts different from that emerged out of the benchmark Notes: This is an illustration of the forecasted path of one of the components generated from STFM, using actual data. Source: Authors’ Estimates. forecast at (t+0) (dotted black line, Chart 5). Using 70 RBI Bulletin November 2025 21-t 11-t 01-t 9-t 8-t 7-t 6-t 5-t 4-t 3-t 2-t 1-t 0+t 1+t 2+t 3+t 4+t 5+t 6+t 7+t 8+t 9+t 01+t 11+t Chart 4: Impact of Some Macro Variables on Headline Inflation (y-o-y) (percentage points [ppt]) a. Exchange rate (10 per cent depreciation) b. Output gap (1 ppt increase) c. Diesel prices (10 per cent increase) 1 0.25 1 0.9 0.9 0.8 0.20 0.8 0.7 0.7 0.6 0.15 0.6 0.5 0.5 0.4 0.10 0.4 0.3 0.3 0.2 0.05 0.2 0.1 0.1 0 0.00 0 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 Horizon (Months) Horizon (Months) Horizon (Months) Source: Authors’ Estimates Months‘Making the Horizons Meet’: A Heterodox Approach for ARTICLE Short-Term Inflation Forecasting Chart 6: Density Forecasts (y-o-y, per cent) a. Forecasts of SD of Errors b. Density Forecasts 2.5 2.0 1.5 1.0 0.5 0.0 Notes: 1. This is an illustration of forecast path of one of the components generated from STFM, using actual data. 2. In Chart b, the thick black shaded area represents 50 per cent confidence interval, implying that there is 50 per cent probability that the actual outcome will be within the range given by the thick black shaded area. Likewise, for 70 per cent and 90 per cent confidence intervals, there is 70 per cent and 90 per cent probability, respectively, that the actual outcomes will be in the range represented by the respective shaded areas. Source: Authors’ Estimates. forecast, even after 12-months, thus, leaving some models) in terms of accuracy for generating forecasts lasting impact. in short-term horizon, relative to other models is already established in the Indian context (Mohan et The SDs of the forecast errors for each sub- al., 2025). Thus, the proposed framework leverages group/component that are also generated from this the advantage of nowcasts in the near-term, while framework, are then used for creating the density ensuring enhanced forecast accuracy in the short- forecasts. An illustrative example of a group in CPI term. However, the overall accuracy of this framework basket is demonstrated in Chart 6. Chart 6.a provides depends on the accuracy of the nowcasts. This the uncertainty around the point forecasts through horizons, which are measured using SDs. Estimated Chart 7: Pseudo-out of Sample Root Mean Square SDs are then used to generate the density forecast Errors of Headline Inflation (y-o-y) (Chart 6.b). (percentage points) 1.2 1.1 Finally, the evaluation of the short-term 1.0 forecasting model is carried out by generating pseudo- 1.0 0.9 0.9 0.9 0.9 1.0 out of sample root mean square errors (RMSE) 0.8 (Chart 7). 0.5 0.6 Pseudo-out of sample RMSE for y-o-y headline 0.4 inflation is markedly lower in the near-term compared to benchmark forecasts indicating the advantage of 0.2 0.2 full information based nowcasts in the near-term. As 0.0 the horizon extends the accuracy of the short-term 0 1 2 3 4 56 Months ahead forecasts converges to that of the benchmark forecasts. Benchmark Forecasts Nowcasts Short-term Forecasts The advantage of the benchmark model (based on Note: Lower RMSEs indicate better forecast accuracy. Source: Authors’ Estimates inflation combination approach of a large suite of RBI Bulletin November 2025 71 0+t 1+t 2+t 3+t 4+t 5+t 6+t 7+t 8+t 9+t 01+t 11+t 10 8 6 4 2 0 Months Months 6-t 5-t 4-t 3-t 2-t 1-t 0+t 1+t 2+t 3+t 4+t 5+t 6+t 7+t 8+t 9+t 01+t 11+tARTICLE ‘Making the Horizons Meet’: A Heterodox Approach for Short-Term Inflation Forecasting underscores the need for a consistent and accurate Del Negro, M., & Schorfheide, F. (2013). DSGE framework for generating nowcasts, rather than the model-based forecasting. In Handbook of economic full information matrix–based system presented in forecasting (Vol. 2), 57-140. Elsevier. this article. Ideally, such a framework should integrate Eberhart, R., & Kennedy, J. (1995, October). A new high-frequency, spatial, and multi-source data sets–an optimizer using particle swarm theory. In MHS’95. area identified for future research. Proceedings of the sixth international symposium on IV. Conclusion micro machine and human science, 39-43. IEEE. This paper presents a framework for short- Haario, H., Saksman, E., & Tamminen, J. (2001). An term inflation forecasting that bridges data-driven modelling, machine learning techniques, structural Adaptive Metropolis Algorithm. Bernoulli, 223-242. hysteresis, macro-linkages, and inter-sectoral Hastings, W. (1970). Monte Carlo sampling spillovers. By integrating nowcasts, benchmark methods using Markov chains and their forecasts, seasonal factors, and judgmental applications. Biometrika, 57(1), 97-109. adjustments into a dynamic system of disaggregated component/sub-group level equations, this John, J., Singh, S., & Kapur, M. (2020). Inflation Forecast framework offers a forward-looking and granular Combinations: The Indian Experience. Reserve Bank view of inflation dynamics. The design’s flexibility of India Working Paper Series No. 11. also enables scenario analysis. Importantly, the Kolsrud, D. (2007). Time-simultaneous prediction disaggregated architecture allows for clear attribution to inflation formation. In this framework, the band for a time series. Journal of Forecasting, 26(3), enhanced forecast performance in the near-horizon 171-188. stemming from nowcasts is accounted for, still Lucas Jr, R. E. (1976, January). Econometric policy preserving the advantage of statistical and machine evaluation: A critique. In Carnegie-Rochester learning models in short-horizon. It is also equipped conference series on public policy (Vol. 1), 19-46. with generating density forecasts. As such, this North-Holland. forecasting framework provides a powerful, yet pragmatic solution for generating short-term inflation Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. forecasts, in an increasingly complex and uncertain N., Teller, A. H., & Teller, E. (1953). Equation of state environment, which are peculiar characteristics of an calculations by fast computing machines. The journal emerging market economy. of chemical physics, 21(6), 1087-1092. References: Mohan, R., Hasan, S., Roy, S., Sarkar, S., and John, J. Barratt, S. T., & Boyd, S. P. (2020, July). Fitting a (2025) Predicting CPI inflation in India: Combining Kalman smoother to data. In 2020 American Control Forecasts from a ‘Suite’ of Statistical and Machine Conference (ACC), 1526-1531. IEEE. Learning Models, RBI Bulletin, June. Brooks, S. (1998). Markov chain Monte Carlo method and its application. Journal of the royal statistical Monsell, B. C., Lytras, D., & Findley, D. F. (2016). society: series D (the Statistician), 47(1), 69-100. Getting Started with X-13 ARIMA-SEATS Input Files. US Das, P. & George, A. (2023). Consumer Price Index: The Census Bureau. Center for Statistical Research and Aggregation Method Matters. RBI Bulletin, March. Methodology, ML. 72 RBI Bulletin November 2025Multivariate Core Trend Inflation: A New Measure of Core Inflation ARTICLE Multivariate Core Trend Therefore, the concept of trend inflation is pivotal to monetary policy, as it represents the level where Inflation: A New Measure of actual inflation is expected to stabilise. Core Inflation The Multivariate Core Trend (MCT) inflation model, originally developed to analyse inflation within by Harendra Kumar Behera and the personal consumption expenditures (PCE) price Abhishek Ranjan^ index, offers a sophisticated approach to dissecting inflation trends across multiple sectors. This model’s This study estimates Multivariate Core Trend (MCT) relevance extends beyond its initial application, inflation by using disaggregated CPI series with dynamic providing valuable insights into the persistent and sectoral weights. By assigning time-varying weights based transitory components of inflation. on sectoral volatility, persistence, and co-movement, our The motivation of applying the MCT model to model identifies whether underlying inflation is driven the Indian context stems from the need to address by broad-based trend or sector-specific forces. The results the complex and varied inflationary pressures across show a stable contribution of sector-specific trends, while different sectors of the economy. Traditional measures changes in the common trend – comprising both volatile of inflation often fail to capture the nuanced and components (food and fuel) and more stable components sector-specific trends that can significantly impact (core) – largely determine the dynamics of MCT. We economic stability and growth. By employing the MCT find that MCT inflation tracks both core and headline model, policymakers can gain a deeper understanding inflation more effectively over longer horizons, offering a of these dynamics, enabling more precise and effective more reliable gauge of underlying inflationary pressures. interventions. Introduction The economic policymakers are particularly interested in distinguishing between temporary price Understanding inflation dynamics is critical for fluctuations and more entrenched inflation trends. designing effective economic policies, particularly for This distinction is vital for making informed decisions a country like India which emphasises price stability on interest rates and other monetary policy tools. as the primary goal of its monetary policy. Moreover, The MCT model’s ability to decompose inflation into accurately estimating trend inflation – vital for common trends, sector-specific trends, and transitory forecasting the future trajectory of inflation – becomes shocks provides a clearer picture of the underlying a key focus for the monetary policy formulation and drivers of inflation. decision-making after the introduction of flexible inflation target framework in June 2016. However, Furthermore, as India continues to integrate into isolating the core inflationary pressures from the the global economy, the ability to accurately measure overall inflation rate is a complex challenge. Various and respond to inflationary pressures becomes forms of ‘noise’ influences aggregate inflation, increasingly important. The insights gained from the making it difficult to differentiate between long-term MCT model can enhance the Reserve Bank’s capacity to persistent trends and short-term cyclical fluctuations. maintain price stability, a key objective in its monetary policy framework. This, in turn, supports sustainable ^ The authors are from the Department of Economic and Policy Research, economic development and helps to mitigate the Reserve Bank of India. The views expressed in this article are those of the authors and do not represent the views of the Reserve Bank of India. adverse effects of inflation on the broader economy. RBI Bulletin November 2025 73ARTICLE Multivariate Core Trend Inflation: A New Measure of Core Inflation The application of the MCT model in India is, Internationally, multivariate unobserved- therefore, motivated by the need to better understand components frameworks similar to the MCT model and manage inflation dynamics in a complex and have been used across multiple economies. Stock and evolving economic landscape. By providing a more Watson (2016) demonstrate the multivariate trend detailed and accurate measure of inflation, the MCT structure for US inflation, motivating the Federal model supports more effective economic policies Reserve Bank of New York’s real-time MCT estimates and contributes to the overall stability and growth for the US. Thailand adopted disaggregated UC-SV of the Indian economy. The paper is organised in the models to extract trend inflation (Manopimoke & following section: Section II presents the literature Limjaroenrat, 2017), while the European Central review and Section III highlights some facts about Bank has applied factor-based and state-space filters inflation. Section IV describes the model and Section to capture underlying inflation pressures across V provides the empirical estimates and decomposition heterogeneous euro-area economies. Central banks in of the MCT. Section VI analyses the predictive power New Zealand and Australia also employ disaggregated of the MCT inflation for both core and headline. trend models to address commodity-price shocks and Section VII concludes the paper. housing-services persistence. Similarly, Korea and II. Literature Review Japan have utilized UC-SV and factor-trend frameworks in low-inflation environments to detect turning Core inflation, as introduced by Eckstein (1981), points in persistent inflation. Emerging economies is “the rate [of inflation] that would occur on the such as Brazil and South Africa have examined UC- economy’s long-term growth path, provided the based filters to account for large food-price shocks path were free of shocks, and the state of demand were neutral in the sense that markets were in long- and exchange-rate pass-through. These experiences run equilibrium”. The concept of core inflation is underscore the value of multivariate, time-varying subsequently refined and articulated as the ‘central frameworks in economies where supply shocks are bank view’ of inflation, achieved by excluding or sizeable and traditional exclusion-based measures minimizing the impact of specific factors–especially may understate persistent pressures. those that are volatile or erratic (Blinder, 1982; Apel Against this background, the application of the and Jansson, 1999). Thus, core inflation (i.e., inflation MCT model to India is both relevant and timely, excluding food and fuel components), is considered as given India’s high food weight in CPI, repeated supply standard benchmark for the trend inflation. shocks, and documented spillovers from food to Core-inflation measurement has evolved from core inflation. The multivariate framework enables simple exclusion-based approaches to more structural extraction of the true underlying trend by allowing and model-based methods. In the Indian context, time-varying persistence, volatility, and co-movement exclusion of food and fuel remains the conventional across CPI components – providing richer information benchmark, followed by trimmed-mean, weighted- than conventional core measures. median, re-weighted CPI, UC-SV, and PCA-based trend III. Some Facts about Inflation metrics (George et al., 2024; Patra et al., 2024a; Behera and Patra, 2022). These approaches, while useful, A cross-country comparison of CPI basket do not fully account for time-varying persistence, compositions reveals significant variation in the sector-specific dynamics, and spillovers from volatile weight of food and fuel across economies, reflecting components. structural differences in consumption patterns. In 74 RBI Bulletin November 2025Multivariate Core Trend Inflation: A New Measure of Core Inflation ARTICLE to Myanmar (58.5 per cent) and followed by Thailand Table 1: Weights of Food and Energy in Consumer (40.4 per cent) among the sample. Price Index (Per cent) This pattern underscores the vulnerability of Country Food Fuel /Energy Source inflation in emerging markets to supply-side shocks, US (Urban) 14.41 6.66 CEIC (2023) especially from food and energy prices. The high UK 11.9 14.1 CEIC (2023) weight of food in India’s CPI, for example, amplifies Germany 12.66 5.35 HICP (2022) France 16.56 4.73 HICP (2022) the transmission of volatile food price movements Japan 26.26 6.93 CEIC (2023) to headline inflation. While fuel weights are more India 45.86 6.84 CEIC (2023) consistent across countries (generally between 5-7 per China 19.9 2~3 Bloomberg Brazil 21.11 11.46 CEIC (2023) cent), exceptions such as the UK (14.1 per cent) and Australia 17.18 4.52 CEIC (2023) Brazil (11.5 per cent) reflect distinct energy pricing Philippines 37.73 6.74 CEIC (2023) structures or broader definitions of household energy Indonesia 25.01 5.81 CEIC (2023) consumption. These structural differences justify Myanmar 58.46 8.08 CEIC (2022) the need for country-specific core inflation measures Thailand 40.35 5.49 CEIC (2023) Sources: CEIC; HICP; and Bloomberg and motivate multivariate approaches like the MCT framework – that can distinguish between transitory advanced economies such as the US, UK, Germany, and persistent components across heterogeneous CPI and France, the combined weight of food and fuel is structures. relatively low, typically ranging between 15 and 30 per cent (Table 1). In contrast, emerging market economies In Indian context, median inflation measures – particularly in Asia – exhibit much higher weights. indicate a period of relative stability from January For instance, India stands out with food accounting 2015 to early 2020, with values generally ranging for nearly 46 per cent of its CPI basket, only second between 4 and 6 per cent (Chart 1)1. This was followed Chart 1: Median Inflation (Per cent) 8 7 6 5 4 3 2 1 0 Median inflation Weighted median inflation CPI excluding food and fuel Median core inflation Weighted median core Source: MOSPI; and RBI staff estimates 1 Core inflation in India is commonly defined as CPI excluding food & beverages and fuel & light. Alternative operational definitions – such as excluding only food or excluding volatile subgroups – exist in literature but are not used in this baseline specification. RBI Bulletin November 2025 75 51-naJ 51-yaM 51-peS 61-naJ 61-yaM 61-peS 71-naJ 71-yaM 71-peS 81-naJ 81-yaM 81-peS 91-naJ 91-yaM 91-peS 02-naJ 02-yaM 02-peS 12-naJ 12-yaM 12-peS 22-naJ 22-yaM 22-peS 32-naJ 32-yaM 32-peS 42-naJ 42-yaM 42-peS 52-naJARTICLE Multivariate Core Trend Inflation: A New Measure of Core Inflation by a brief dip around mid-2020, likely reflecting the Patra et al., (2024a) investigates core-like properties of immediate economic impact of the pandemic. From food inflation, viz. volatility, persistence, spillovers late 2021, inflation rose sharply, peaking near 7 per and cyclical sensitivity. George et al., (2024) investigates cent by mid-2022. A sustained disinflationary phase several core measures based on exclusion, trimmed then took hold, bringing median inflation down to means, reweighted CPI, and trend CPI (HP, CF, PCA around 3 per cent by late 2024. Notably, core median etc). However, the inflation measure introduced in inflation – excluding volatile components – closely this paper captures the time varying persistence and tracked the overall trend, pointing to broad-based second round effects2 of the inflation components in movements in underlying inflation throughout the the multivariate core trend (MCT). The inclusion of period. On the contrary, CPI inflation excluding food the contribution from volatile component, food and and fuel components moved upward since mid-2024. fuel, through common trend, is further justified by This requires further investigation of the sources Patra et al., (2024b) which provides empirical evidence driving the underlying dynamics of inflation in India. to suggest the spillover from food inflation to non- food components. The MCT measure of inflation can be helpful in tracking headline for countries where food and fuel IV. Model Description inflation are volatile and has significant weight in We use a multivariate unobserved components the headline inflation. The concept of MCT measure, model with stochastic volatility and outlier based on the methodology proposed by Stock and adjustments (MUCSVO) to estimate MCT Core Watson (2016), is popularised by the Federal Reserve inflation (Stock and Watson, 2016). The model uses Bank of New York (FRBNY) after Almuzara and disaggregated sectoral data to estimate the underlying Sbordone (2022) published an article in Liberty Street trend through time-series smoothing methods. The Economics to explain the sources of inflation surge in basic premises of the model is based on decomposition the post-pandemic period. The FRBNY publishes the of actual inflation into its permanent and transitory MCT inflation for the US on Monday following the components. The model decomposes the inflation of release of the PCE data. The MCT inflation measure ith sector at time t (π ) into common trend (τ ), sector i,t c,t accounts for time-varying weights influenced by specific trend (τ ), sector specific common shocks i,t the volatility, persistence, and co-movement of (ϵ ) and sector specific idiosyncratic shocks (ϵ ). The c,t i,t different sectoral inflation. Inflation of each sector is coefficients of common trend (i.e., ) and common decomposed into four components: a common trend, shocks (i.e., ) are modelled as time-varying. The i,τ,t α a sector-specific trend, a common transitory shock, trend components follow a martingale process, and i,ϵ,t α and a sector-specific transitory shock. CPI inflation the transitory components are assumed to be serially trend is estimated by adding the common trend uncorrelated. Innovations to both the trends and the with the sector-specific trends weighted by the CPI transitory components have time-varying variances Core weights. Trend decomposition can further help (σ σ σ σ ) that follow logarithmic random τ,c,t ϵ,c,t τ,i,t ϵ,i,t identify the source of inflation persistence (Almuzara wa2lk sto2chas2tic vo2latility processes. Common factor Δ Δ ; ; ; and Sbordone (2022)). The persistence and spillover and specific sector are indicated by the subscripts c from the volatile components of the CPI contributes and i, respectively. The specification of multivariate to MCT inflation through the common trend. model is given below: There has been series of studies in the Bulletin of 2 Behera, H. K. & Ranjan, A. (2024), “Food and Fuel Prices: Second Round Reserve Bank of India around alternate core measures. Effects on Headline Inflation in India” RBI Bulletin, April 76 RBI Bulletin November 2025Multivariate Core Trend Inflation: A New Measure of Core Inflation ARTICLE π α τ α ϵ τ ϵ (1) V. Empirical Results i,t i,τ,t c,t i,ϵ,t c,t i,t i,t τ c,t = τ c,t σ +τ,c,t η τ,c+,t + (2) We estimate MCT inflation for India using ϵ σ –1 s Δ η (3) item-level CPI data from January 2012 to March c,t = ϵ,c,t +c,t ϵ*,c,t τ τ σ η (4) 2025. The 9-digit-level CPI items are grouped into i,t= i,t * Δτ*,i,t τ,i,t 23 subcategories: 12 under Food and Beverages, 6 ϵ σ –1 s η (5) i,t= ϵ,i,t +i,t ϵ*,i,t under Miscellaneous, 2 under Clothing and Footwear, α α λ ζ (6) i,τ ,=t i,τ ,*t *i,τ i,τ,t and one each under Pan, Tobacco and Intoxicants, α α –1 λ ζ (7) i,ϵ,t= i,ϵ,t + i,ϵ i,ϵ,t Housing, and Fuel and Light4. Using the methodology (σ )–1 γ ν (8) = τ,c,t + τ,c τ,c,t outlined in the previous section and sub-group level 2 Δ ln(σΔ ϵ,c,t) = γ ϵΔ ,c + ν ϵ,cΔ ,t (9) CPI weights, we compute MCT inflation for India. 2 (σΔ ) γ ν (10) Δln τ,i,t = τ,i+ τ,i,t Before turning to the multivariate trend estimates, 2 Δ ln(σΔ ϵ,i,t) = γ ϵΔ ,i + ν ϵ,iΔ ,t (11) we first estimate univariate measures of trend 2 ΔWlhnereΔ the = distu+rbances (η τ,c,t η ϵ,c,t η τ,i,t η ϵ,i,t ζ i,τ,t ζ i,ϵ,t inflation using the Unobserved Components model ν ν ν ν ) are independent and identically with Stochastic Volatility and Outlier Adjustments Δτ,c,t ϵ,c,t Δτ,i,t ϵ,i,t , , , , , , distributed standard normal. The multivariate model (UCSVO), applied separately to headline and core , , , allows the outliers in inflation, which occur each CPI. The UCSVO specification mirrors the MUCSVO period with probability p and p, respectively in model but excludes the common shock or common c i common shocks and sector specific shocks, through trend structure5. The univariate trend estimates independent and identically distributed normal suggest that inflation was generally on a declining random variables s and s (Stock and Watson, 2016). trajectory until the starting of the COVID-19 pandemic c i (Chart 2). Supply disruptions during the pandemic The aggregate trend inflation (τ) is given by t and the Ukraine war led to a temporary rise in trend weighted sum of the sectoral trends where w is the i inflation until end-2022. Subsequently, trend inflation weight or contribution of ith sector in the combined declined, reaching 4.3 per cent by March 2025. consumer price index. τ n w (α τ τ ) (12) The UCSVO results also indicate that the volatility t i i i,τ,t c,t i,t of trend in headline CPI is higher than that of core Where= 1the weights are normalised and summed = ∑ + inflation, reflecting the persistence of food and up for the core inflation to get multivariate core trend energy shocks6. This highlights the potential bias in inflation. The model is estimated by using Bayesian conventional core inflation measures that exclude methods. The posterior parameters of the model are food and fuel components without accounting for estimated by using Bayesian MCMC method, with their persistent effects (Stock and Watson, 2016; 6000 iterations of which 3000 are being used as burn Manopimoke and Limjaroenrat, 2017). However, the in iterations. We use an inverse gamma prior for λ by choosing scale and shape parameters consistent temporary rise in stochastic volatility in core trend with T Prior prior observations (T Prior T where T is the 4 Details of data and sectoral weights are provided in the Appendix. sample size) and s = (Del Negro and Otrok, 5 See more Stock and Watson (2016) for more details on UCSVO. Prior T Prior2 = 10 2008)3. 2 0.25 6 Model-detected outliers align with major macroeconomic disruptions: (i) nationwide lockdown and mobility restrictions (Q1-Q2 2020), (ii) supply 3 For detailed estimation approach including choice of number of chain normalisation with inventory restocking (late-2020), and (iii) global iteration and burnouts, please refer to online appendix of Stock and energy and commodity price surge after the Russia-Ukraine war (2022). Watson (2016). https://www.princeton.edu/~mwatson/papers/core_and_ These outliers reflect non-systematic, high-amplitude price movements trend_inflation_online_appendix_20151103.pdf that do not represent persistent inflation dynamics. RBI Bulletin November 2025 77ARTICLE Multivariate Core Trend Inflation: A New Measure of Core Inflation Chart 2: UCSVO Estimates a. Trend Inflation b. Stochastic Volatility in Trend Inflation (Per cent) (Per cent) 10 8 6 4 2 0 Core Headline Core Headline c. Stochastic Volatility in Observed Inflation (Per cent) Core Headline Source: RBI staff estimates. inflation reflects pandemic and war related supply contributions vary over time due to time-varying disruptions that affected core services and goods, not coefficients on the common trend (α ). The estimated i,τ,t just food and fuel. Elevated logistics costs, medical MCT inflation is considerably smoother and less expenses, and administered price adjustments volatile than both headline and core CPI (Chart 3). widened inflation persistence across core items, Between January 2012 and March 2025, headline blurring the historical separation between volatile and inflation had a volatility of 2.16, core inflation 1.59, “sticky” CPI groups. and MCT inflation of just 1.25. Although core CPI and MCT inflation tend to move together, core CPI appears For both core and headline CPI, the stochastic volatility of observed inflation remains higher than to lag MCT. Both measures have remained below 4 per that of the trend inflation, though both have declined cent in recent months. MCT inflation rose from April steadily over time. Interestingly, volatility in core 2019 to April 2022, then declined, suggesting a fall in inflation was initially higher than headline inflation inflation persistence. but reversed after September 2015. To understand this recent decline in trend To estimate MCT inflation, we use only the core inflation, we decompose MCT inflation into common CPI components, with their weights normalised to and sector-specific components. Each subgroup one. While these core weights are fixed, sectoral contributes to MCT through two channels: a common 78 RBI Bulletin November 2025 21-naJ 21-luJ 31-naJ 31-luJ 41-naJ 41-luJ 51-naJ 51-luJ 61-naJ 61-luJ 71-naJ 71-luJ 81-naJ 81-luJ 91-naJ 91-luJ 02-naJ 02-luJ 12-naJ 12-luJ 22-naJ 22-luJ 32-naJ 32-luJ 42-naJ 42-luJ 52-naJ 0.1 0.09 0.08 0.07 0.06 0.05 0.04 0.03 21-naJ 21-luJ 31-naJ 31-luJ 41-naJ 41-luJ 51-naJ 51-luJ 61-naJ 61-luJ 71-naJ 71-luJ 81-naJ 81-luJ 91-naJ 91-luJ 02-naJ 02-luJ 12-naJ 12-luJ 22-naJ 22-luJ 32-naJ 32-luJ 42-naJ 42-luJ 52-naJ 2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 21-naJ 21-luJ 31-naJ 31-luJ 41-naJ 41-luJ 51-naJ 51-luJ 61-naJ 61-luJ 71-naJ 71-luJ 81-naJ 81-luJ 91-naJ 91-luJ 02-naJ 02-luJ 12-naJ 12-luJ 22-naJ 22-luJ 32-naJ 32-luJ 42-naJ 42-luJ 52-naJMultivariate Core Trend Inflation: A New Measure of Core Inflation ARTICLE trend and a subgroup-specific trend. The common trend component. Thus, contribution of any specific trend captures persistent shocks, including those sector to MCT inflation comprise of both common from food and fuel, while the subgroup-specific trend component and its own trend component trends reflect stickiness of core CPI components. (Chart 4). While subgroup-specific contributions are This structure allows MCT to capture second-round relatively stable, changes in the common trend – effects more effectively than traditional core inflation comprising both volatile components (food and fuel) measures. The contribution of common trend in MCT and more stable components (core) – largely determine depends on the time varying weight attached to the the dynamics of MCT. Chart 4: Decomposition of MCT in common and sector specific trends (Percentage Points) 10 8 6 4 2 0 -2 Source: RBI staff estimates. RBI Bulletin November 2025 79 21-naJ 21-luJ 31-naJ 31-luJ 41-naJ 41-luJ 51-naJ 51-luJ 61-naJ 61-luJ 71-naJ 71-luJ 81-naJ 81-luJ 91-naJ 91-luJ 02-naJ 02-luJ 12-naJ 12-luJ 22-naJ 22-luJ 32-naJ 32-luJ 42-naJ 42-luJ 52-naJ Chart 3: Headline Inflation, Core Inflation, and Multivariate Core Trend Source: RBI staff estimates. MCT Common trend Sector specific trendARTICLE Multivariate Core Trend Inflation: A New Measure of Core Inflation A further decomposition reveals that contributions clothing and footwear together with pan, tobacco from transport and communication, clothing, and and intoxicants; housing; and miscellaneous. household goods and services have declined over Contributions from each major group are derived time, while the role of personal care and effects from their constituent subgroups. MCT inflation has increased (Chart 5). This changing composition estimated from group-level components differs from offers insight into the evolving sources of inflation that aggregated from subgroup-level estimates, due persistence. to differences in both common and sector specific We also explore a less granular decomposition trend dynamics. The results show a clear declining by aggregating subgroups into broader categories: contribution of clothing and footwear and a recent increase in contribution of miscellaneous categories Chart 6: Decomposition of Multivariate Core Trend in aggregated sectors to overall MCT inflation (Chart 6). (Percentage points) 8 Additionally, we classify inflation into goods and services: core goods, core services, non-core 6 goods, and non-core services. Table 2 presents the number of items and their respective weights in each 4 category. Table 2: CPI sectors with their weights in 2 Headline CPI Category Number of Items CPI Weights 0 Core Goods 122 0.2425 Core Services 39 0.2305 Clothing, footwear, pan & tobacco Housing Non-Core Goods 137 0.5238 Miscellaneous Multivariate core trend Non-Core Services 1 0.0032 Sources: CEIC; and RBI staff estimates. Sources: MOSPI; and RBI staff estimates. 80 RBI Bulletin November 2025 02-naJ 02-rpA 02-luJ 02-tcO 12-naJ 12-rpA 12-luJ 12-tcO 22-naJ 22-rpA 22-luJ 22-tcO 32-naJ 32-rpA 32-luJ 32-tcO 42-naJ 42-rpA 42-luJ 42-tcO 52-naJ Chart 5: Decomposition of Multivariate Core Trend (Percentage Points) 8 7 6 5 4 3 2 1 0 -1 Pan, tobacco and intoxicants Transport and communication Housing Recreation and amusement Footwear Education Clothing Personal care and effects Household goods and services MCT Health Source: RBI staff estimates. 02-naJ 02-rpA 02-luJ 02-tcO 12-naJ 12-rpA 12-luJ 12-tcO 22-naJ 22-rpA 22-luJ 22-tcO 32-naJ 32-rpA 32-luJ 32-tcO 42-naJ 42-rpA 42-luJ 42-tcO 52-naJMultivariate Core Trend Inflation: A New Measure of Core Inflation ARTICLE Chart 7: Decomposition of Multivariate Core Trend – Goods and Services Category (Percentage Points) 7 6 5 4 3 2 1 0 Core goods Core services Multivariate core trend Sources: MOSPI; and RBI staff estimates. The decomposition of MCT inflation by goods and VI. Predictive Power services reveals no significant shift in contribution We assess the predictive power of MCT inflation in patterns overall (Chart 7). However, while goods forecasting both core and headline inflation. Forecast historically contributed more to MCT, the share of performance is evaluated using out-of-sample root services has risen in recent months. mean square error (RMSE) for different time horizons This version of MCT inflation, constructed using (1, 3, 6, and 12 months ahead). Lower RMSE values goods and services decomposition, is also smoother imply better predictive accuracy. A baseline model using the most recent observed value of headline than core and headline CPI inflation. Between January inflation (i.e., a random walk) is used as a benchmark, 2015 and March 2025, headline and core CPI inflation with its RMSE normalized to 1. Relative RMSEs for had volatilities of 1.39 and 0.91, respectively, compared other models are then calculated. to just 0.69 for MCT inflation. Table 3 summarises the descriptive statistics of all inflation measures, Forecasts are evaluated over the test set from with MCT2 covering the period from January 2015 to April 2024 to March 2025 using rolling RMSEs. Results March 2025. show that MCT significantly improves forecast Table 3: Descriptive Statistics of Various Measures of Inflation Measures of Inflation Minimum Maximum Mean Median Standard Skewness Kurtosis Deviation Food -1.69 16.65 6.15 6.19 3.61 0.16 -0.32 Fuel -5.48 14.35 5.25 5.32 4.26 -0.24 -0.40 Core 3.12 10.35 5.55 5.19 1.59 1.03 0.79 Headline 1.46 11.51 5.80 5.41 2.16 0.59 -0.24 MCT (subgroup) 2.67 8.80 4.87 4.83 1.25 0.75 0.75 MCT2(Goods/Services) 3.25 6.13 4.73 4.80 0.69 -0.35 -0.43 Sources: CEIC; and RBI staff estimates. RBI Bulletin November 2025 81 02-naJ 02-yaM 02-peS 12-naJ 12-yaM 12-peS 22-naJ 22-yaM 22-peS 32-naJ 32-yaM 32-peS 42-naJ 42-yaM 42-peS 52-naJARTICLE Multivariate Core Trend Inflation: A New Measure of Core Inflation Table 4: Relative RMSE for Headline and Core Inflation Forecasts Model RMSE (Headline) RMSE (Core) Number of Months 1 3 6 12 1 3 6 12 Model with lagged Headline 1.00 1.00 1.00 1.00 - - - - Model with lagged Core 1.01 0.96 0.92 0.88 1.00 1.00 1.00 1.00 Model with lagged MCT (Subgroup) 0.98 0.95 0.88 0.84 0.76 0.71 0.72 0.78 Model with lagged MCT2 (Goods/Services) 0.98 0.90 0.84 0.80 0.82 0.47 0.31 0.70 Source: RBI staff estimates. performance, particularly for core inflation, and has gradually become a more prominent contributor consistently outperforms the benchmark across all to the MCT in the post-pandemic period, consistent horizons. For headline inflation, MCT improves long- with evolving consumption patterns and supply-chain horizon forecasts, but gains are limited at shorter normalisation. horizons –likely due to its focus on persistent trends Forecast evaluation confirms the usefulness rather than short-term volatility in non-core items. of MCT: although short-horizon gains are modest, Notably, forecasts based on MCT2 (estimated from MCT demonstrates superior predictive accuracy for goods and services decomposition) outperform the core inflation at medium- and long-term horizons, broader MCT model (Table 4). outperforming random-walk and conventional core VII. Conclusion benchmarks. These results reinforce the importance This paper develops a Multivariate Core Trend of using flexible, disaggregated trend-extraction (MCT) inflation measure for India using a Bayesian frameworks when commodity-price shocks and multivariate unobserved-components model with supply-chain rigidities drive headline inflation stochastic volatility, calibrated on disaggregated dynamics. CPI components. The MCT framework provides However, MCT measure is not simple to interpret, a structural advantage over exclusion-based core and is difficult to communicate. Further, the choice of inflation metrics by allowing time-varying sectoral disaggregation can have impact on the MCT measure. sensitivities, explicitly modelling both common and The MCT decomposition can be used for tracking idiosyncratic trends, and capturing second-round early signal of persistence and second round effects of effects from food and fuel through the common trend inflation components. channel. Overall, the findings suggest that MCT inflation is Empirically, MCT inflation is smoother and less a robust operational measure of underlying inflation volatile than both headline and conventional core pressures and can complement existing core measures inflation. It leads core inflation during turning points, suggesting its usefulness as an early signal of evolving in India’s monetary policy framework. Future work inflation persistence. The decomposition shows that can explore high-frequency extensions, real-time the common trend – combining persistent pressures filtering properties, and state-dependent dynamics from both core and volatile components – explains – particularly for differentiating supply-driven and much of the recent inflation cycle, with sector-specific demand-driven inflation persistence in India’s persistence relatively stable. Notably, services inflation evolving macro-financial environment. 82 RBI Bulletin November 2025Multivariate Core Trend Inflation: A New Measure of Core Inflation ARTICLE References Eckstein, O. (1981). Core Inflation, Engelwood Cliffs, N.J, Prentice-Hall 1981. Almuzara, M. and Sbordone, A. (2022) Inflation Persistence: How Much Is There and Where Is Eichengreen, B., & Gupta, P. (2024) Inflation Targeting in India: A Further Assessment, NCAER Working Paper It Coming From?, Federal Reserve Bank of New #174. York, Liberty Street Economics, April 20, 2022, Retrieved from https://libertystreeteconomics. George, A.T., Bhatia, S., John, J. & Das, P. (2024), newyorkfed.org/2022/04/inflation-persistence-how- Headline and Core Inflation Dynamics: Have the much-is-there-and-where-is-it-coming-from/. Recent Shocks Changed the Core Inflation Properties for India?, RBI Bulletin, February, 2024. Apel, M., and Jansson, P. (1999), A Parametric Approach Manopimoke, P., & Limjaroenrat, V. (2017). Trend for Estimating Core Inflation and Interpreting the inflation estimates for Thailand from disaggregated Inflation Process, Stockholm, Sweden, Sveriges data. Economic Modelling, 65, 75-94. Riksbank Working Paper Series, 80. Ministry of Finance (2024), Economic Survey 2023- Behera, H. K., & Patra, M. D. (2022), Measuring Trend 24, New Delhi: Ministry of Finance, Department of Inflation in India. Journal of Asian Economics, 80, Economic Affairs. 101474. Patra, M.D., John, J. & George, A.T. (2024a), Are Food Behera, H. K. & Ranjan, A. (2024), Food and Fuel Prices: Prices the ‘True’ Core of India’s Inflation?, RBI Bulletin, Second Round Effects on Headline Inflation in India, January, 2024. RBI Bulletin, April, 2024. Patra, M.D., John, J. & George, A.T. (2024b), Are Food Blinder, A.S. (1982). The Anatomy of Double-Digit Prices Spilling Over?, RBI Bulletin, August, 2024. Inflation in the 1970s. In R.E. Hall (ed.), Inflation: Stock, J.H., Watson, M.W., (2007). Why has US inflation Causes and Effects, 261-282, University of Chicago become harder to forecast? Journal of Money, Credit & Press. Banking 39 (s1), 3-33. Chhibber, A. (2020), With Food and Fuel Consumer Stock, J.H., Watson, M.W., (2016). Core inflation Price Index Surges, it’s Time to Rethink the Inflation and trend inflation. The Review of Economics and Target Regime, Economic Times (24 January, 2020). Statistics 98 (4), 770-784. RBI Bulletin November 2025 83ARTICLE Multivariate Core Trend Inflation: A New Measure of Core Inflation Appendix Data represents ‘Goods/Services’, 7th and 8th digits gives item serial number within Section and 9th digit The Ministry of Statistics and Program represents identification of items: Weighted Item or Implementation (MOSPI) release inflation data on Priced Item. A sample of the items along with the code 12th day7 of every month. The press release reports is provided in Table A18. headline, and food inflation for India. It also releases disaggregated item level inflation data on its website. 1st digit which represents group are summarized Each item has 9-digit code, and the coding structure in Table A2. Core inflation is obtained by removing has been devised such that each item is identified Group 1 (Food and Beverages), and Group 5 (Fuel and uniquely in various sub-grouping. 1st digit represents Light). ‘Group’, 2nd digit represents ‘Category’ within the 2nd digit represents category within the group. For group, 3rd and 4th digits represent ‘Sub-group’ for example, within Group 1, there are two categories: every group, 5th digit represents ‘Section’, 6th digit Food and Beverages. The code 1.1 represents Food whereas 1.2 represents Beverages. 3rd and 4th digit Table A1: Inflation Item Code represents subgrouping within a group. For example, Item_Code Item Food and Beverages are further subgrouped in Cereal 1.1.01.1.1.01.P Rice – PDS 1.1.01.1.1.16.0 Cereal Substitutes: Tapioca, Etc. and Products, Meat and Fish, Egg, Milk and Products, 1.1.01.2.1.01.X Jowar & Its Products Oils and Fats, Fruits, Vegetables, Pulses and Products, 1.1.01.3.2.01.0 Grinding Charges Sugar and Confectionery, Spices, Non-alcoholic 1.1.02.2.1.01.X Fish, Prawn Beverages, and Prepared meals, snacks, sweets etc. 1.1.12.3.1.03.X Prepared Sweets, Cake, Pastry Similarly other groups can be classified into different 1.2.11.2.1.01.0 Mineral Water (Litre) subgroups9. 5th digit is the section within subgroup. For 2.1.01.1.1.01.0 Country Liquor (Litre) example, Cereal and Products are further subclassified 2.1.01.3.1.08.0 Other Tobacco Products 3.1.01.1.1.02.0 Saree (No.) into different sections: Major cereals and products, 3.1.01.1.1.03.X Shawl, Chaddar (No.) Coarse cereals and products, and Grinding charges. 6th 3.1.01.4.2.01.X Washerman, Laundry, Ironing digit is 1 or 2 based on the item type. 1 represents 4.1.01.1.2.01.X House Rent, Garage Rent Goods, whereas 2 is for Services. 7th and 8th digit is 4.1.01.2.2.02.X Water Charges 4.1.01.2.2.03.0 Watch Man Charges (Other Cons Taxes) Table A2: Inflation Item Code 5.1.01.2.1.01.X LPG [Excl. Conveyance] 1st Digit of Item Code Group 5.1.01.3.1.01.P Kerosene – PDS (Litre) 1 Food and Beverages 5.1.01.3.1.02.0 Kerosene – Other Sources (Litre) 2 Pan, Tobacco and Intoxicants 5.1.01.3.1.04.0 Diesel (Litre) [Excl. Conveyance] 3 Clothing and Footwear 6.1.01.1.1.03.X Chair, Stool, Bench, Table 4 Housing 6.1.02.2.2.06.0 Other Medical Expenses (Non-Institutional) 5 Fuel and Light Doctor’s/ Surgeon’s Fee-First Consultation (Non- 6.1.02.2.2.07.0 6 Miscellaneous Institutional) Source: MOSPI. 6.1.02.2.2.08.X X-Ray, ECG, Pathological Test, Etc. (Non-Institutional) Source: MOSPI. 8 For complete table along with their weights, please refer to https://cpi. mospi.gov.in/Weight_AI_Item_Combined_2012.aspx 7 In case of weekend, it releases either on 11th or 13th day of the month. 9 For full classification, please refer MOSPI (2015). 84 RBI Bulletin November 2025Multivariate Core Trend Inflation: A New Measure of Core Inflation ARTICLE for the item serial number within section. 9th digit is use this 9-digit code to compute different Multivariate identification type. 9th digit can be ‘X’, ‘P’ or ‘0’. ‘X’ is Core Trend Inflation. for an item if it has more than one priced item, ‘P’ is The disaggregated subgroups along with their for PDS items, and ‘0’ for all remaining items. MOSPI weights are provided in the Table A3. (2015) provides full details of the classification. We Table A3: CPI Sectors with their Weights in Headline CPI Group Subgroup CPI Weights Cereals and Products 0.0967 Meat and Fish 0.0361 Egg 0.0043 Milk and Milk Product 0.0661 Oils and Fats 0.0356 Fruits 0.0289 Food and Beverages Vegetables 0.0604 Pulses and Products 0.0238 Sugar and Confectionery 0.0136 Spices 0.0250 Non-alcholic Beverages 0.0126 Prepared Meals, Snacks, Sweets, etc 0.0555 Pan, Tobacco and Intoxicants Pan, Tobacco and Intoxicants 0.0238 Clothing 0.0558 Clothing and Footwear Footwear 0.0095 Housing Housing 0.1007 Fuel and Light Fuel and Light 0.0684 Household Goods and Services 0.0380 Health 0.0589 Transport and Communication 0.0859 Miscellaneous Recreation and Amusement 0.0168 Education 0.0447 Personal Care and Effects 0.0389 Source: MOSPI. RBI Bulletin November 2025 85Nowcasting GDP in India: A New Approach ARTICLE Nowcasting GDP in India: macroeconomic indicators, especially GDP, is a necessity for optimal policy response and reliable A New Approach short-term forecasts are generally high in demand when the economic environment is uncertain by Indrajit Roy and K. M. Neelima^ (Hindrayanto, Koopman, & Winter, 2016). Central banks and other policy makers track Nowcasting has become a useful tool for policymakers certain high frequency indicators to gauge the especially for macroeconomic variables like GDP for underlying state of the economy. However, divergence which data are released with considerable lags. A novel among various indicators make it difficult to two-step approach for nowcasting India’s GDP is proposed accurately assess the extant condition of the economy. using twenty-two high frequency indicators wherein the Essentially, separating meaningful information from first factor is obtained based on the strength of each noise is a humongous task and several techniques indicator in relation to GDP and a second factor is ranging from detecting business cycle turning points built from the residual information of the indicators and constructing indexes of economic activity which are otherwise generally discarded. The empirical to forecasting comprehensive macroeconomic exercise reveals that incorporating secondary information measures of the state of the economy with formal from residuals greatly improves accuracy of nowcasting models and judgment have been applied to tackle it GDP. (Bok et al., 2017). Summarising the information Introduction content available from different indicators using Following overlapping shocks—COVID-19 modelling techniques in recent periods into a pandemic, multiple and prolonged geopolitical composite index, or nowcasting, was thus developed conflicts, surge in inflation across the globe—policy to respond to the policymakers’ need for a reliable makers, especially central banks, had their task cut indicator in advance of the release of the relevant out to prop up the economy while managing the macroeconomic variable. Hence, an important feature ramifications emanating from the different shocks. of nowcasting is the extraction of all the available With new challenges emerging often, associated information from a large information set and it uncertainty has made it difficult to assess the current provides an early estimate of the reference series and future outlook of the economy. For policymakers, before it is published. having a clear picture of the underlying state of the Most of the nowcast models generally reduces economy is critical for undertaking appropriate dimensions of large number of selected indicators policy responses. Gross domestic product (GDP) can into few factors. This can be done by dynamic factor be considered as the most authoritative measure modelling (DFM) or even using weighted average of of economic activity (Proietti, Giovannelli, Ricchi, indicators where weights are correlation of indicators & Citton, 2021). The national accounts provide a with the target. High frequency indicators are generally comprehensive view of the economy but are released selected for nowcasting based on convenience and with considerable lags. Therefore, forecasting timely availability of the indicators. As a result, for ^ The authors are from the Monetary Policy Department, Reserve Bank nowcasting a composite target, for example, GDP or of India. The authors are thankful for the comments from anonymous gross value added (GVA), a sub-sector of the target may reviewers and Shri Dipankar Biswas which have significantly enhanced the quality of the article. The authors thank Neha Dahale for data support. be over-represented by inclusion of greater number The views expressed in this article are those of the authors and do not represent the views of the Reserve Bank of India. of selected proxy indicators vis-à-vis other sectors. RBI Bulletin November 2025 87ARTICLE Nowcasting GDP in India: A New Approach Thus, factor modelling, which essentially reduces To compare the nowcasting performance of the dimensionality of large number of indicators TSMIM, following four models are considered: and produces few common factors, is influenced (a) TSMIM-1 with PCI as weighted average by indicator selection bias as the chosen factor may of chosen indicators and SCI as weighted identify the co-movement of the selected indicators average of residuals extracted from these very well, while not necessarily capturing the indicators which are not part of PCI. relationship with the target series truly. (b) Using only PCI as weighted average of chosen It may also be the case that in DFM, information indicators and no SCI. contents of the selected indicators are not completely (c) TSMIM-2 with DFM based factor as the PCI utilised as only the first few latent factors are chosen and SCI as weighted average of residuals based on eigenvalues, and other factors with lower extracted from the indicators which are not part of DFM. eigen values are ignored which, in turn, may be having strong correlation with the reference series (d) Benchmark DFM. but are not strongly correlated with majority of other For ascertaining the efficiency of nowcasting indicators in the information set. exercise, out-of-sample prediction method is undertaken for the period Q4:2022-23-Q4:2024-25. In this article, an attempt has been made to This empirical exercise reveals a relatively improved nowcast India’s GDP using a two-step approach (two- performance of the new framework models (TSMIM-1 stage maximum information model - TSMIM) wherein and TSMIM-2) when compared with one-step models in the first stage a primary composite index (PCI) is (models b and d). computed, by linearly combining these indicators based on the strength of their association (correlation) We further check for robustness of the approach with the targeted series. PCI may also be computed on real GVA growth and find that the new models demonstrate improved performance over DFM for using DFM. GVA as well. Rest of the paper is organised as follows: In the second stage, to further extract relevant brief literature review is undertaken in section II and information from the indicators beyond what is section III elaborates the methodology and data used. already extracted and aggregated in PCI, each indicator Section IV discusses the results and the evaluation of is regressed on the PCI and the corresponding residuals the new model and section V concludes. are estimated which are, in turn, aggregated based II. Literature Review on their association with the target series to form a There are many nowcasting techniques available secondary composite index (SCI). PCI and SCI are then in the literature. Among these techniques, principal jointly used to nowcast GDP. component analysis/dynamic factor models (PCA/ The novelty of our new approach lies in a) giving DFM) to nowcast low frequency macroeconomic more weightage to those indicators that are correlated variable such as GDP is popular. PCA, which is the core with the target series rather than co-movement of of the DFM model for nowcasting, transforms original the indicators among themselves, and b) maximising information set into uncorrelated factors, which information by utilising information which are are the weighted linear combination of constituent discarded by the conventional modelling process by indicators. Thus, it resolves both the curse of creating a secondary information source based on dimensionality issue as well as the multicollinearity residuals. issues. 88 RBI Bulletin November 2025Nowcasting GDP in India: A New Approach ARTICLE A more prevalent approach of nowcasting currently nowcasting (Roy, Sanyal, & Ghosh, 2016; Bragoli & is DFM. This method is widely used in summarising Fosten, 2017; Iyer & Sen Gupta, 2019; Kumar, 2020; co-moving indicators, that cover the broad spectrum of Bhadury, Ghosh, & Kumar, 2021; Prakash, Bhowmick, economic activities in an economy, as latent factor(s) & Thakur, 2022; Kaustubh & Ranjan, 2025). However, separated from idiosyncratic and measurement Matheson (2011) finds that forecasting performance errors and can be interpreted as underlying state of of DFM for Australia and India was not on par with the economy. In the model, a state-space framework other countries which may be attributable to large data is followed which involves a measurement equation revisions of indicators in these countries. Bayesian linking the vector of observed indicators to a vector of vector autoregression (Iyer & Sen Gupta, 2019) and unobserved state variables and a transition equation machine learning approaches (Ghosh & Ranjan, 2023) which specifies the dynamics of the unobserved state are also used for nowcasting. variables. In DFM, the factors are linear combination of Stock and Watson (1989) pioneered the use of constituent indicators. However, as only first few factor models for construction of business cycle components are chosen (with eigenvalue more than indexes. Giannone et al. (2008), introduced factor 1), and other factors with lower eigen values are models for nowcasting in a mixed frequency setup and ignored, there is an inherent loss of information since the nowcasting model was developed using monthly they might have good association with the reference data on a large set of high-frequency indicators. series. It is likely that the factors with lower eigen Many central banks have developed nowcasting value are those factors which are dominated (with methods to get a fair idea about how the economy is higher loading/share) by indicators which may have performing in a given quarter much before the official strong correlation with the reference series but data release. The nowcasting model of the Federal are not strongly correlated with majority of other Reserve Bank of New York uses a dynamic factor indicators in the information set. As a result, there model that generates estimates of current quarter may be factors, which possess relevant information GDP growth at a weekly frequency (Almuzara, Baker, to explain variation in the reference series, but is O’Keeffe, & Sbordone, 2023). On the other hand, The discarded due to low eigen value which result in Federal Reserve Bank of Atlanta’s GDPNow model suboptimal performance in explaining the reference is a nowcasting model that uses a bridge equation series. Our paper focuses on this aspect by obtaining approach that relates GDP subcomponents to monthly additional information set from residuals which may source data with factor model and Bayesian vector help improve the nowcasting technique. autoregression techniques (Higgins, 2014). III. Methodology and Data In India too, several studies have been undertaken III.1 Two-stage Maximum Information Model on nowcasting GDP as GDP data are released by (TSMIM) the National Statistics Office (NSO) with a lag of two months. Bhattacharya et al. (2011) finds that Following Roy and Narayanan (2018), we use a a small set of pre-selected key monthly indicators, two-step process to construct a two-stage maximum serving as proxies for the various sub-sectors of the information model (TSMIM). When the target series is economy perform satisfactorily in predicting current of quarterly frequency and indicators are of monthly quarter growth. Several studies in India use DFM for frequency, the indicators are averaged over three RBI Bulletin November 2025 89ARTICLE Nowcasting GDP in India: A New Approach months to get quarterly series1. These quarterly indicator and these residuals form the constituents of indicators are then standardised by subtracting their the additional information set. mean and dividing by standard deviation. Let r is the correlation coefficient of with y. i it t Let X and Y denote ith indicator and the reference Secondary composite index (SCI) is computed as it t ζ̂ series at time t, respectively, where i = 1, 2….n; t=1, follows: 2….T. Corresponding standardised series, x and y it t SCI k w E (4) t i it i i are defined as: x (X it mean(X it)) y (Y t mean(Y t)) (1) Whe =r∑e, =w1 ζi ̂ = * ∑'' i *Er i i * r i and it [ –sd(X it) ] t [ –sd(Y t) ] E i if ''it is significantly associated with y t in In = the first st;e p, = we calculate correlation equation (3), = 1 ζ̂ coefficient (ρ) of each indicator with the target series. i =0, otherwise. Then primary composite index (PCI) is defined as follows: Notably, this additional information set is built from the residual information of the n-indicators PCI x w' D (2) t i it i i which are not part of PCI. whe r=e ∑ w ' i *∑ i Dρ ii * * ρ i and Therefore, PCI and SCI are two composite indices D i if =it h indicator is significantly associated of coincident indicators for the reference series with y t = 1 constructed out of many indicators and are linear = 0, otherwise. combination of indicators. Also, by construction, PCI t and SCI are uncorrelated, therefore, these can be used Essentially, PCI is a certain linear combination t together to explain Y without any multicollinearity t of selected indicators, and it contains common issue. Nowcasted value of the reference series can be information of interest pertaining to association of obtained as follows: these selected indicators with the reference series. = η + η * PCI + η * SCI + y (5) t t t t In the second step, we look for additional information in the information set beyond PCI. AYt+ 1‘ ’0, valu1 e of P+1C I t ,2 SCI t +1 (η η+1 η ) are Certain components of the reference series may known but Y t is not known +1. The e +s 1tima 0t ed 1 va 2lue of t+1 , ̂ , ̂ , ̂ Y in equation (5) is a weighted average of PCI and be under-represented or over-represented by the t +1 SCI. indicators selected, thus PCI may be influenced by this +1 biased selection. We start with regressing each of the Together, PCI and SCI can explain the reference indicator on the PCI and estimating the corresponding series much better than PCI alone. residuals as follows: The benchmark DFM model is described in Let x it η i δ i * PCI t ζ it (3) Annex A. Where= ζ it ~+ i.i.d. N (+0 , ), η i and δ i are unknown III.2 Data coefficients. 2 i ψ For nowcasting a reference series, a set of Let where i = 1, 2,….n be the residuals indicators is chosen based on the strength of their it estimated from equation (3) corresponding to ith connection or relationship (correlation coefficients, ζ̂ visual inspection from scatter plot) with the reference 1 If data for a variable is unavailable for a particular month, it is forecasted using ARIMA. series. We initially followed Kumar (2020) for variable 90 RBI Bulletin November 2025Nowcasting GDP in India: A New Approach ARTICLE Table 1: List of Indicators Industry Services Global Miscellaneous Index of Industrial Production Domestic air passenger traffic US Industrial Production Gross taxes Automobile sales Domestic air cargo traffic Baltic Dry Index JobSpeak Index Non-oil exports Port cargo traffic OECD Composite Leading Indicator Crude price (average of Brent, Dubai and WTI) Non-oil-non-gold imports Railway freight US payrolls Purchasing Managers’ Index - Mfg. Foreign tourist arrivals Power supply Purchasing Managers’ Index - Services Fuel consumption IIP Cement Steel consumption Source: Authors’ compilation. selection as the 27 indicators in the model were based IV. Results: Nowcasting of India’s GDP and Model on whether they are tracked by NSO, their correlation Evaluation with GDP, and availability of time series data. However, Strength of association, in terms of correlation we find that five variables viz., US PMI, non-food coefficients, of selected indicators with the chosen credit, tractor sales, CPI excluding food and beverage target series viz., GDP is given in Annex Table and money supply were not significantly correlated A1. Estimates of equation (3) for deriving second with GDP and were dropped from the model (Annex information set and equation (4) for correlation of Table A1). We use a set of 22 indicators2 which are residuals with GDP are reported in Annex Tables A2 significantly correlated with GDP (Table 1). and A3, respectively. Broadly, these indicators cover major segments IV.1 Model Evaluation of domestic activity- directly or indirectly. The four To assess the performance of the proposed blocks of data are a) industry; b) services, c) global model, empirical exercise to nowcast real GDP using and d) miscellaneous. The data includes a) hard the following four models were undertaken: data on economic activity for example, index of industrial production, automobile sales, port cargo (a) TSMIM-1 with PCI as weighted average of traffic, domestic air cargo traffic etc., b) surveys indicators and SCI as weighted average of representing economic activity like PMI, c) trade residuals extracted from the indicators which are not part of PCI. such as non-oil exports, non-oil imports, Baltic Dry Index etc., d) employment conditions as captured by (b) Using only PCI as weighted average of chosen JobSpeak Index and e) global conditions as captured indicators and no SCI. by OECD composite leading indicator, US payroll (c) TSMIM-2 with DFM based factor as the PCI data and US industrial production. All indicators and SCI as weighted average of residuals are in year-on-year terms. The nowcasting exercise extracted from the indicators which are not is undertaken separately for each quarter for the part of DFM. period Q4:2022-23 – Q4:2024-25 using data spanning (d) Benchmark DFM. Q1:2011-12 – Q4:2024-25. The results were also compared with median 2 The data were winsorised at 0 and 99.25 levels to adjust for outliers caused by COVID-19 pandemic. forecasts of survey of professional forecasters (SPF). RBI Bulletin November 2025 91ARTICLE Nowcasting GDP in India: A New Approach The in-sample model fit using results of the Table 2: GDP Regression Estimates nowcast exercise undertaken for the latest quarter, Dependent Variable: GDP viz., Q4:2024-25 is given below. The coefficients— Method: Least Squares Sample (adjusted): 2012Q2-2024Q4 PCI, SCI and dynamic factor (DF)— are significant Variable TSMIM-1 PCI TSMIM-2 DFM suggesting that these factors can explain real GDP (model a) (model b) (model c) (model d) growth. Further, the different models for nowcasting DF 1.64*** 1.63*** GDP show that the in-sample fit of (model a) TSMIM-1, (0.06) (0.10) PCI 0.40*** 0.41*** and (model c) TSMIM-2 are better than that of using (0.02) (0.02) benchmark DFM model (model d), and using only SCI 2.43*** 2.57*** PCI (model b) on the basis of R-square. Using a single (0.25) (0.29) factor - DF or PCI - explains around 85 per cent of C 6.17*** 6.22*** 6.24*** 6.17*** (0.18) (0.31) (0.179) (0.30) the variability of GDP (model b and model d) while Observations 51 51 51 51 using two factors together explain 94 per cent of the R-squared 0.94 0.83 0.94 0.85 variability of GDP suggestive of superior performance Adjusted 0.94 0.83 0.94 0.84 R-squared of using secondary factor in both cases (Table 2). Notes: 1. Correlation of GDP with PCI is 0.9 and with SCI is 0.3. 2. Standard deviation of PCI is much higher than SCI. Visual presentation of nowcasted GDP series 3. Figures in parentheses are standard error. using both DFM and TSMIM-1 undertaken for the 4. * p<0.10, ** p<0.05, *** p<0.010. 5. Correlation coefficients of indicators greater than 0.1 with quarter Q4:2024-25 vis-à-vis actual GDP is provided in GDP is considered as significant while computing composite indicators PCI and SCI. Chart 1. Nowcasted GDP using TSMIM-1 was found to be closely following the target variable. performance is also compared against projections The results for out-of-sample nowcasts for the of the latest round of the survey of professional study period comparing the model estimates vis-à-vis forecasters (SPF). We calculate error (actual-nowcast) the quarterly estimates of GDP data released by MOSPI for each quarter and average root mean squared error on May 30, 2025 is given in Table 3. The nowcast (RMSE) for all models for the period under study. Chart 1: Nowcasted GDP and Actual GDP Series- Exercise Undertaken for Q4:2024-25 (Per cent) 30 20 10 0 -10 -20 -30 GDP Nowcast- TSMIM-1 Nowcast- DFM Sources: National Statistics Office; and Authors’ calculations. 92 RBI Bulletin November 2025 71-6102:1Q 71-6102:2Q 71-6102:3Q 71-6102:4Q 81-7102:1Q 81-7102:2Q 81-7102:3Q 81-7102:4Q 91-8102:1Q 91-8102:2Q 91-8102:3Q 91-8102:4Q 02-9102:1Q 02-9102:2Q 02-9102:3Q 02-9102:4Q 12-0202:1Q 12-0202:2Q 12-0202:3Q 12-0202:4Q 22-1202:1Q 22-1202:2Q 22-1202:3Q 22-1202:4Q 32-2202:1Q 32-2202:2Q 32-2202:3Q 32-2202:4Q 42-3202 :1Q 42-3202 :2Q 42-3202 :3Q 42-3202:4Q 52-4202:1Q 52-4202:2Q 52-4202:3Q 52-4202:4QNowcasting GDP in India: A New Approach ARTICLE Table 3: GDP Nowcast Performance Period Nowcast GDP Error (Actual-Forecast)2 actual data TSMIM-1 PCI TSMIM-2 DFM SPF TSMIM-1 PCI TSMIM-2 DFM SPF Q4:2022-23 8.57 6.71 8.81 6.92 4.60 6.90 2.81 0.03 3.66 0.00 5.28 Q1:2023-24 8.21 5.51 8.08 5.90 7.50 9.66 2.11 17.20 2.49 14.12 4.67 Q2:2023-24 9.08 7.27 9.04 7.80 6.30 9.34 0.07 4.30 0.09 2.37 9.26 Q3:2023-24 7.49 7.10 7.66 7.56 6.50 9.51 4.10 5.83 3.43 3.82 9.09 Q4:2023-24 6.87 7.15 7.06 7.38 6.00 8.35 2.21 1.46 1.67 0.94 5.54 Q1:2024-25 6.89 6.73 7.05 6.98 7.00 6.51 0.14 0.05 0.29 0.22 0.24 Q2:2024-25 5.56 5.16 6.18 5.65 7.00 5.61 0.00 0.20 0.32 0.00 1.93 Q3:2024-25 6.53 5.48 6.82 5.76 6.40 6.37 0.03 0.79 0.21 0.37 0.00 Q4:2024-25 6.91 5.92 7.16 6.00 7.00 7.38 0.23 2.15 0.05 1.92 0.15 Root Mean Squared Error (RMSE) 1.14 1.89 1.16 1.62 2.00 RMSE excluding Q1: 2023-24 1.09 1.36 1.10 1.10 1.98 Note: Single factor models recorded a large error in Q1:2023-24. Hence, RMSE excluding Q1: 2023-24 is also presented. Sources: NSO; and authors’ calculations. RMSE of SPF and model (b) were found to be IV.1.a Robustness Checks the highest among all the sets of nowcasts. RMSE The same exercise was undertaken for nowcasting of DFM is the next highest primarily on account of real gross value added (GVA) growth at basic prices the inability of the model to predict the growth in for the same period. The regression estimates for Q1:2023-24. TSMIM-1, followed by TSMIM-2 had Q4:2024-25 for GVA show that the coefficients of the variables of interest are significant in all models. As the lowest RMSE in the period under study crucially in the case of GDP, the model fit of TSMIM-1, and underpinning the role of secondary information in TSMIM-2 are better than that of using only DF in the improving nowcast accuracy even while excluding case of GVA as well (Table 4). Q1:2023-24 from calculation of RMSE. The models using SCI have performed consistently better than the Chart 2: GDP Nowcast Performance (Per cent) models using only single factor in most quarters in 11 nowcasting GDP (closer to the actual estimates) from 10 the information content available through the same 9 8 set of twenty-two indicators. 7 Chart 2 plots the nowcasts generated for each 6 5 quarter vis-à-vis actual GDP and SPF forecast. Notably, 4 during the period of high growth in 2023-24, TSMIM-1 3 and TSMIM-2 nowcasts were closer to the latest 2 GDP estimates than those generated by using only DF and PCI in single-step models underpinning higher accuracy achieved due to the use of secondary GDP actual data TSMIM-1 TSMIM-2 PCI DFM SPF factor. SPF forecasts were found to be less accurate till Sources: National Statistics Office; and Authors’ calculations. recently. RBI Bulletin November 2025 93 32-2202:4Q 42-3202 :1Q 42-3202 :2Q 42-3202 :3Q 42-3202:4Q 52-4202:1Q 52-4202:2Q 52-4202:3Q 52-4202:4QARTICLE Nowcasting GDP in India: A New Approach Table 4: GVA Regression Estimates Chart 3: GVA Nowcast Performance (Per cent) Dependent Variable: GVA 11 Method: Least Squares 10 Sample (adjusted): 2012Q2-2024Q4 Variable TSMIM-1 PCI TSMIM-2 DFM 9 (Model a) (Model b) (Model c) (Model d) 8 DF 1.50*** 1.50*** (0.05) (0.09) 7 PCI 0.37*** 0.37*** 6 (0.01) (0.02) 5 SCI 2.25*** 2.235*** (0.23) (0.26) 4 C 6.03*** 6.08*** 6.11*** 6.04*** (0.16) (0.28) (0.16) (0.27) Observations 51 51 51 51 R-squared 0.95 0.84 0.94 0.86 GVA actual data TSMIM-1 TSMIM-2 PCI DFM SPF Adjusted 0.95 0.83 0.94 0.86 R-squared Sources: National Statistics Office; and Authors’ calculations. Notes: 1. Figures in parentheses are standard error. 2. * p<0.10, ** p<0.05, *** p<0.010 3. Correlation coefficients of indicators greater than 0.1 with TSMIM for nowcasting GVA is lower than that of GVA are considered significant while computing composite nowcasting GDP reflective of indicators capturing indicators PCI and SCI. economic activity from supply side (Table 5). Further, like GDP, forecasted series of GVA using all models track actual GVA well for the exercise Visually, TSMIM-based nowcasts track GVA more undertaken for Q4:2024-25 (Annex Chart A1). RMSE closely than DFM based nowcasts, especially in the of nowcasts for real GVA growth generated using latest quarters, wherein the nowcasts generated for TSMIM-1 and TSMIM-2 are the lowest among all models, which improves further if Q1:2023-24 is each vintage vis-à-vis actual GVA based on latest data excluded from the sample. Incidentally, RMSE of are plotted (Chart 3). Table 5: GVA Nowcast Performance Period Nowcast GVA Error (Actual-Forecast)2 actual data TSMIM-1 PCI TSMIM-2 DFM SPF TSMIM-1 PCI TSMIM-2 DFM SPF Q4:2022-23 7.99 6.51 7.96 6.73 6.70 6.60 1.92 0.01 1.84 0.02 5.28 Q1:2023-24 7.71 5.44 7.56 5.79 7.10 9.94 4.99 20.28 5.65 17.17 4.67 Q2:2023-24 8.84 7.11 9.02 7.56 6.20 9.22 0.15 4.46 0.04 2.76 9.26 Q3:2023-24 7.58 6.97 7.91 7.34 6.20 8.00 0.17 1.06 0.01 0.43 9.09 Q4:2023-24 7.08 6.98 7.27 7.15 5.50 7.27 0.04 0.08 0.00 0.01 5.54 Q1:2024-25 6.94 6.57 7.35 6.76 6.40 6.55 0.15 0.00 0.64 0.05 0.24 Q2:2024-25 5.57 5.12 6.08 5.55 6.80 5.81 0.06 0.48 0.07 0.07 1.93 Q3:2024-25 6.41 5.42 6.61 5.66 6.40 6.49 0.01 1.16 0.01 0.70 0.00 Q4:2024-25 6.73 5.81 6.89 5.89 6.70 6.77 0.00 0.91 0.01 0.78 0.15 Root Mean Squared Error (RMSE) 0.91 1.78 0.96 1.56 1.65 RMSE excluding Q1: 2023-24 0.56 1.01 0.57 0.78 1.44 Note: Single factor models recorded a large error in Q1:2023-24. Hence, RMSE excluding Q1: 2023-24 is also presented. Sources: National Statistics Office; and Authors’ calculations. 94 RBI Bulletin November 2025 32-2202:4Q 42-3202 :1Q 42-3202 :2Q 42-3202 :3Q 42-3202:4Q 52-4202:1Q 52-4202:2Q 52-4202:3Q 52-4202:4QNowcasting GDP in India: A New Approach ARTICLE V. Conclusion in the first stage of TSMIM and in the second stage, residual information are extracted from the coincident Many important low-frequency macro-economic indicators which are not part of the dynamic factor indicators such as GDP are subject to publication obtained from DFM and these residuals are combined delays or lag. However, there are many high frequency into a second factor with a suitable weights (such as coincident indicators which are correlated with the correlation with the reference series). DF and second targeted macroeconomic indicator that are available factor thus obtained produced improved nowcast at much shorter time lags. Monitoring all these of the reference series than nowcasts generated by coincident indicators and revising the assessment benchmark DFM alone. Incorporating secondary about the reference series is a difficult task for the information from residuals greatly improves accuracy policy makers. Combining all these coincident of nowcasting and therefore, TSMIM is an important indicators into a composite index for nowcasting addition to the nowcasting toolkit. was thus developed to meet the need for reliable References indication in advance of the release of the relevant macroeconomic indicator. Almuzara, M., Baker, K., O’Keeffe, H., & Sbordone, A. (2023). The New York Fed Staff Nowcast 2.0. New York This article uses a new framework (TSMIM), to Fed Staff Technical Paper. extract maximum information relevant to nowcast the reference series. Coincident indicators are Bhadury, S. S., Ghosh, S., & Kumar, P. (2021). combined into a weighted composite index (PCI), Constructing a coincident economic indicator for which generally tracks the reference series well. India: How well does it track gross domestic product? However, residual information may contain some Asian Development Review. more information for the reference series which are Bhattacharya, R., Pandey, R., & Veronese, G. (2011). not completely captured by PCI. Therefore, the new Tracking India Growth in Real Time. National Institute approach further extracts information which is not of Public Finance and Policy, Working Papers 2011/90. part of the already calculated composite index i.e., Bok, B., Caratelli, D., Giannone, D., Sbordone, A., & PCI, and has potential to be related to the reference Tambalotti, A. (2017). Macroeconomic Nowcasting series. These secondary indicators derived from the and Forecasting with Big Data. Federal Reserve Bank primary set of indicators are then again combined of New York Staff Reports. into a SCI with suitable weights. Bragoli, D., & Fosten, J. (2017). Nowcasting Indian GDP. This article shows a relatively improved Oxford Bulletin of Economics and Statistics. performance (both in-sample and out of sample) of Ghosh, S., & Ranjan, A. (2023). A Machine Learning the new framework (TSMIM) when compared with the Approach To GDP Nowcasting: An Emerging Market baseline pure dynamic factor-based modelling (DFM) Experience. Bulletin of Monetary Economics and based nowcasting. TSMIM-based nowcasts track GDP Banking. and GVA more closely than pure DFM based nowcasts, when the nowcasts generated for each vintage vis-à- Giannone, D., Reichlin, L., & Small, D. (2008). vis the actual official data are compared. Moreover, Nowcasting: The Real Time Informational Content of TSMIM framework can accommodate DFM model and Macroeconomic Data. Journal of Monetary Economics, can reduce forecast errors further. DFM can be used 665-676. RBI Bulletin November 2025 95ARTICLE Nowcasting GDP in India: A New Approach Higgins, P. (2014). GDPNow: A Model for GDP Prakash, A., Bhowmick, C., & Thakur, I. (2022). “Nowcasting”. Federal Reserve Bank of Atlanta Nowcasting of India’s GDP using dynamic factor Working Paper. model: Optimising the results. The Journal of Income & Wealth. Hindrayanto, I., Koopman, S. J., & Winter, J. d. (2016). Forecasting and nowcasting economic growth in the Proietti, T., Giovannelli, A., Ricchi, O., & Citton, A. euro area using factor models. International Journal of (2021). Nowcasting GDP and its components in a data- Forecasting, 1284-1305. rich environment: The merits of the indirect approach. Iyer, T., & Sen Gupta, A. (2019). Nowcasting International Journal of Forecasting, 1376–1398. economic growth in India: The role of rainfall. Asian Roy, I., & Narayanan, K. (2018). Pull Factors of FDI: A Development Bank Economics Working Paper Series. Cross-Country Analysis of Advanced and Developing Iyer, T., & Sen Gupta, A. (2019). Quarterly Forecasting Countries. In N. Siddharthan, & K. Narayanan, Model for India’s Economic Growth: Bayesian Vector Globalisation of Technology. India Studies in Business Autoregression Approach. ADB Working Paper Series. and Economics. Singapore: Springer. Kaustubh, K., & Ranjan, A. (2025). A multi-factor GDP Roy, I., Sanyal, A., & Ghosh, A. K. (2016). Nowcasting nowcast model for India. Economic Modelling. Indian GVA Growth in a Mixed Frequency Setup. Kumar, P. (2020). An Economic Activity Index for Reserve Bank of India Occasional Papers, 97-107. India. RBI Bulletin. Stock, J., & Watson, M. (1989). New Indexes of Matheson, T. (2011). New Indicators for Tracking Coincident and Leading Economic Indicators. Growth in Real Time. International Monetary Fund Macroeconomics Annual, National Bureau of Working Paper No. WP/11/43. Economic Research, 351-409. 96 RBI Bulletin November 2025Nowcasting GDP in India: A New Approach ARTICLE Annex A: Dynamic Factor Model A dynamic factor model assumes that many Equations 6-8 constitute a state-space model observed variables (y , , y ) are driven by a few with equation 6 being the observation equation and i,t n,t unobserved dynamic factors (f , . . ., f ), and specific equations 7-8 representing transition equations. State ,t r,t ... features of distinct series such 1as measurement errors, variables and parameters of the state-space model are captured by idiosyncratic errors (e ,t , , e n,t ). The are estimated using Kalman filter algorithm (Bok general specification of a dynamic factor model is: 1 et al., 2017). The model is considered particularly ... y λ f … λ f e , i 1,…,n (6) suitable for monitoring macroeconomic conditions it i, ,t i,r r,t i,t in real time as it provides flexibility to incorporate wh=e re1 y i1 s a+re th+e high+-f reque=ncy indicators, f js are data with mixed frequency, missing values and non- the latent common factors, and λ are factor loadings ijs synchronous releases. of factor f on indicator y. The error term, e , captures j i it the idiosyncratic component of each indicator . In the second step, the dynamic common factor f t Common factors and idiosyncratic components are is used to nowcast current quarter GDP growth using i modelled as autoregressive processes: a bivariate regression accounting for serial correlation f a f u , u ~iidN( ,σ 2) (7) in errors.3 The model specification is given below. j,t j j,t– j,t j,t uj e i,t = ρ i e i,t1 –+ ε i,t, ε i,t~iidN(0,σ ε2 i ) (8) GDPGr t β β f t u t (9) 1 0 1 = + 0 = + + 3 The monthly dynamic factor obtained from twenty-two monthly high-frequency indicators is converted into quarterly frequency by simple averaging. The quarterly series is then used in the regression model to map to the quarterly target variable which is GDP. RBI Bulletin November 2025 97ARTICLE Nowcasting GDP in India: A New Approach Annex A Table A1: Correlation of Indicators with Real GDP Growth during June 2011- March 2025 Sl. No Indicators Correlation Coefficient Sl. No Indicators Correlation Coefficient 1 IIP 0.946*** 15 Gross Taxes 0.687*** (0.000) (0.000) 2 Domestic air cargo traffic 0.868*** 16 Railway freight 0.668*** (0.000) (0.000) 3 Fuel Consumption 0.851*** 17 Non-oil Non Gold Imports 0.546*** (0.000) (0.000) 4 US Payroll 0.815*** 18 Non-oil Exports 0.515*** (0.000) (0.000) 5 PMI Services 0.792*** (0.000) 19 Foreign Tourist Arrivals 0.449*** (0.001) 6 Steel Consumption 0.785*** (0.000) 20 OECD CLI 0.405*** (0.002) 7 Port cargo traffic 0.770*** (0.000) 21 Crude Prices 0.398*** 8 IIP Cement 0.763*** (0.002) (0.000) 22 Baltic Dry Index 0.255* 9 Automobile sales 0.752*** (0.057) (0.000) 23 US Purchasing Managers Index 0.216 10 Power Supply 0.741*** (0.110) (0.000) 24 Non Food Credit 0.189 11 PMI Manufacturing 0.738*** (0.163) (0.000) 25 Farm Tractor Sales 0.126 12 US IIP 0.738*** (0.356) (0.000) 13 Naukri Jobspeak Index 0.699*** 26 CPI Excluding Food and Beverage 0.0764 (0.000) (0.576) 14 Domestic air passenger traffic 0.692*** 27 Money Supply M3 -0.0874 (0.000) (0.522) Notes: 1. p-values in parentheses. 2. * p<0.10 ** p<0.05 *** p<0.01 98 RBI Bulletin November 2025Nowcasting GDP in India: A New Approach ARTICLE Annex A Table A2: Coefficients of PCI and DF Regressed on Indicators Indicators PCI DF Coefficient R2 Adj_R 2 Coefficient R2 Adj_R 2 Domestic air cargo traffic 0.079*** 0.853 0.851 0.316*** 0.87 0.867 (0.000) (0.000) Domestic air passenger traffic 0.070*** 0.677 0.671 0.274*** 0.654 0.648 (0.000) (0.000) Automobile sales 0.067*** 0.613 0.605 0.271*** 0.639 0.632 (0.000) (0.000) Baltic Dry Index 0.037*** 0.188 0.173 0.144** 0.181 0.166 (0.001) (0.001) Crude Prices 0.053*** 0.382 0.371 0.190*** 0.315 0.303 (0.000) (0.000) Fuel Consumption 0.068*** 0.638 0.632 0.266*** 0.615 0.608 (0.000) (0.000) Gross Taxes 0.067*** 0.612 0.605 0.267*** 0.621 0.614 (0.000) (0.000) IIP 0.079*** 0.866 0.864 0.319*** 0.883 0.881 (0.000) (0.000) IIP Cement 0.067*** 0.617 0.61 0.258*** 0.581 0.573 (0.000) (0.000) Naukri Jobspeak Index 0.069*** 0.656 0.65 0.267*** 0.622 0.615 (0.000) (0.000) Non-oil Exports 0.059*** 0.484 0.474 0.227*** 0.447 0.437 (0.000) (0.000) Non-oil Non-Gold Imports 0.064*** 0.575 0.567 0.241*** 0.505 0.496 (0.000) (0.000) OECD CLI 0.042*** 0.246 0.232 0.179*** 0.279 0.265 (0.000) (0.000) US Payroll 0.063*** 0.543 0.534 0.240*** 0.503 0.494 (0.000) (0.000) PMI Manufacturing 0.068*** 0.646 0.64 0.285*** 0.704 0.699 (0.000) (0.000) PMI Services 0.076*** 0.794 0.791 0.310*** 0.834 0.83 (0.000) (0.000) Port cargo traffic 0.067*** 0.612 0.605 0.271*** 0.64 0.634 (0.000) (0.000) Power Supply 0.065*** 0.587 0.579 0.254*** 0.56 0.552 (0.000) (0.000) Railway freight 0.073*** 0.729 0.724 0.287*** 0.716 0.711 (0.000) (0.000) Steel Consumption 0.071*** 0.703 0.698 0.297*** 0.766 0.762 (0.000) (0.000) Foreign Tourist Arrivals 0.054*** 0.405 0.394 0.204*** 0.361 0.349 (0.000) (0.000) US IIP 0.068*** 0.645 0.639 0.258*** 0.578 0.57 (0.000) (0.000) Notes: 1. p-values in parentheses. 2. * p<0.10 ** p<0.05 *** p<0.01 RBI Bulletin November 2025 99ARTICLE Nowcasting GDP in India: A New Approach Annex A Table A3: Correlation of GDP with PCI Residual and DF Residual Variable PCI DF Domestic air cargo traffic 0.0663 0.0663 (0.627) (0.627) Domestic air passenger traffic -0.101 -0.0867 (0.459) (0.525) Automobile sales 0.0623 0.0291 (0.648) (0.831) Baltic Dry Index -0.156 -0.151 (0.252) (0.268) Crude Prices -0.211 -0.143 (0.118) (0.292) Fuel Consumption 0.204 0.209 (0.132) (0.122) Gross Taxes -0.0456 -0.0641 (0.739) (0.639) IIP 0.268** 0.241* (0.046) (0.074) IIP Cement 0.0763 0.0966 (0.576) (0.479) Naukri Jobspeak Index -0.0675 -0.0422 (0.621) (0.758) Non-oil Exports -0.166 -0.134 (0.222) (0.326) Non-oil Non-Gold Imports -0.223* -0.153 (0.098) (0.260) OECD CLI -0.0540 -0.0945 (0.693) (0.488) US Payroll 0.212 0.231* (0.118) (0.087) PMI Manufacturing 0.00938 -0.0608 (0.945) (0.656) PMI Services -0.0455 -0.116 (0.739) (0.393) Port cargo traffic 0.0906 0.0569 (0.506) (0.677) Power Supply 0.0655 0.0794 (0.631) (0.561) Railway freight -0.212 -0.206 (0.116) (0.128) Steel Consumption 0.0373 -0.0406 (0.785) (0.766) Foreign Tourist Arrivals -0.170 -0.129 (0.211) (0.344) US IIP 0.00924 0.0599 (0.946) (0.661) Notes: 1. p-values in parentheses. 2. * p<0.10 ** p<0.05 *** p<0.01 100 RBI Bulletin November 2025Nowcasting GDP in India: A New Approach ARTICLE Annex Chart A1: Nowcasted GVA and Actual GVA Series- Exercise Undertaken for Q4:2024-25 (Per cent) 30 20 10 0 -10 -20 -30 GVA Nowcast- TSMIM-1 Nowcast- DFM Sources: National Statistics Office; and Authors’ calculations. RBI Bulletin November 2025 101 71-6102:1Q 71-6102:2Q 71-6102:3Q 71-6102:4Q 81-7102:1Q 81-7102:2Q 81-7102:3Q 81-7102:4Q 91-8102:1Q 91-8102:2Q 91-8102:3Q 91-8102:4Q 02-9102:1Q 02-9102:2Q 02-9102:3Q 02-9102:4Q 12-0202:1Q 12-0202:2Q 12-0202:3Q 12-0202:4Q 22-1202:1Q 22-1202:2Q 22-1202:3Q 22-1202:4Q 32-2202:1Q 32-2202:2Q 32-2202:3Q 32-2202:4Q 42-3202 :1Q 42-3202 :2Q 42-3202 :3Q 42-3202:4Q 52-4202:1Q 52-4202:2Q 52-4202:3Q 52-4202:4QSeasonality in Key Economic Indicators of India ARTICLE Seasonality in Key Economic time series data to better identify underlying long- term trend, cyclical movements and temporary Indicators of India changes, thereby providing a clearer picture of economic conditions. Since 1980, the Reserve Bank by Souvik Ghosh, Shivangee Misra, has been publishing monthly seasonal factors for key Anirban Sanyal and Sanjay Singh^ macroeconomic indicators.1 This article presents the estimates of seasonal This article unveils the seasonal patterns in key patterns in key economic indicators for India. economic indicators for India, analysing 78 monthly Economic activity came to a halt in 2020 due to the indicators across six major sectors—monetary and disruptions caused by the COVID-19 pandemic, banking, payment systems, prices, industrial production, followed by a gradual return to normalcy. This merchandise trade and services along with 25 quarterly entire episode induced severe volatility in major indicators spanning national accounts, balance of macroeconomic variables, characterised by shortlived payments, capacity utilisation of Indian manufacturing inter-temporal changes. In light of this, the analysis companies and forward looking enterprise surveys. Large of seasonal factors accounts for possible changes in seasonal fluctuations were noted in various economic the temporary disruptions of these economic series indicators, including cash balances with RBI, demand before teasing out the seasonal patterns. To ensure the deposits, vegetable prices, production across sectors and robustness of the findings, the stability of seasonal merchandise exports. The quarterly data reveal increased patterns is also cross-validated using pre-pandemic seasonality in real GDP, influenced by government data. expenditure. Among supply side components, GVA The rest of the article is organised as follows: agriculture demonstrated the highest seasonal variations. Section II describes the data and methodology. Section Lastly, capacity utilisation and services exports also III illustrates seasonal factor estimates and discusses experienced high seasonal variation. seasonal variations in the selected economic series. Introduction The article concludes by summarising the findings in Section IV. Seasonality in macroeconomic indicators refers to recurring, predictable patterns that occur II. Data and Methodology within a year. It is a fundamental component of the The article covers the seasonality analysis of data generating process, alongside trend, cyclical major economic indicators at monthly and quarterly variation and random fluctuations. Various factors, frequency. The quarterly variables were included for such as weather conditions, production cycles, the the first time in the analysis since the last release of nature of economic activity, holidays and vacation this article in November 2024. periods influence the seasonal pattern of economic The monthly variables span six key thematic indicators. Seasonal adjustment involves removing areas: monetary and banking statistics, price indices, these recurring patterns and calendar effects from industrial production, services sector indicators, ^ The authors are with the Department of Statistics and Information Management, Reserve Bank of India. The authors are thankful to Shri Ravi 1 First article in the series was published in December 1956 issue of Shankar for his encouragement and guidance in preparing this article. The the Reserve Bank of India Bulletin and annual articles were published views expressed in this article are of the authors and do not represent the since January 1980. The previous article in this series was published in views of the Reserve Bank of India. November 2024 issue of the RBI Bulletin. RBI Bulletin November 2025 103ARTICLE Seasonality in Key Economic Indicators of India merchandise trade and payment systems. A III. Seasonality in Major Economic Variables in comprehensive list of the 78 indicators under these India categories is provided in Table A1-M1 (Annex I). III.1. Seasonality in Monthly Series The quarterly series includes data on national Most of the economic variables examined in the accounts, capacity utilisation (CU) and new orders study display stable seasonal patterns. Among the 14 from the order books, inventories and capacity selected monetary and banking indicators, 11 show utilisation survey (OBICUS), business assessment seasonal peaks in either March or April, while 5 exhibit and expectations indices, component series from troughs in August. Reserve money, narrow money the industrial outlook survey (IOS) and external and bank credit typically peak in March, whereas trade in services from the balance of payments (BoP) broad money and aggregate deposits of scheduled statistics. A full list of the 25 quarterly series used in commercial banks (SCBs) reach their seasonal high the seasonality analysis is available in Table A2-Q1 in April. Within aggregate deposits, demand deposits show a seasonal surge in March, while time deposits (Annex II). peak in April. Loans, cash credits and overdrafts by Seasonal factors are estimated using a SCBs and non-food credit follow a similar seasonal multiplicative time series model with the X13- peak in March. Seasonal trough occurs in February ARIMA-SEATS software developed by the U.S. Census for aggregate deposits and in August for bank credit. Bureau, adapted to Indian conditions by incorporating SCBs’ investments tend to be higher in September and adjustments for Diwali and Indian trading day decline in March. Currency in circulation increases effects. The pandemic-infused volatility in the seasonally in April and tapers off in September (Table economic series and temporary changes in their data A1-M2, Annex I). generating process are adjusted using an automatic Among the monetary and banking indicators, outlier detection mechanism through three types of seasonal variations, measured by the range of the outliers, namely additive outliers (AO), temporary seasonal factors, are high in demand deposits, SCBs’ changes (TC) and level shifts (LS), which are checked cash in hand and balances with RBI and narrow money. subsequently to justify the economic interpretation. Seasonal variations in demand deposits remained The seasonal factor estimates are provided in terms steadfast at 5.5 percentage points in 2024-25, is lower of last 10 years average, last year estimates, range of than its last ten years’ average. On the other hand, seasonal variations defined by the difference between the range of seasonal variations in the SCBs’ cash in the maximum and minimum of seasonal factors and hand and balances with RBI gradually increased over model diagnostics. Recognising that the lack of longer time and touched the highest value of 8.0 percentage time series data for the post-pandemic period may points during 2024-25 (Table A1-M3, Annex I). influence outlier detection and thereby, influence Seasonal pattern in consumer price index- seasonal factor estimates, robustness checks are combined (CPI-C) shows that headline CPI typically carried out by comparing the seasonal factor estimates attains seasonal peak in October and touches of the pre-COVID sample (Technical Annex)2. seasonal lowest by March, largely driven by the food and beverage component. Within food items, 2 The forecast of seasonal factors can be derived using the RegARIMA vegetables exhibit the most pronounced seasonal model fitted on the series. However, it may be noted that the possible changes in the data generating process after the COVID-19 pandemic, may price fluctuations, with tomatoes, potatoes, and influence the model choice and thereby, may impact the forecasts of the seasonal factors. onions (TOP) contributing significantly to overall 104 RBI Bulletin November 2025Seasonality in Key Economic Indicators of India ARTICLE variation. Potato and onion prices rise in November, milder as compared to headline CPI-C during 2024-25 with seasonal pressures easing by March and May, (Chart 2 and Tables A1-M2, A1-M3). respectively, while tomato prices increase in July and Seasonal peak in wholesale prices generally moderate by March (Chart 1 and Table A1-M2). Protein happen in November, while easing is in January items such as meat, fish and eggs also display notable (Chart 3). The wholesale price index (WPI) for primary seasonality. In contrast, other major groups like articles showed a seasonal variation of 4.6 percentage clothing and footwear, housing and miscellaneous points in 2024-25, remaining close to last year’s level items experience relatively lower seasonal variation of 4.4 percentage points. While seasonal variations in (Table A1-M3). WPI food articles have gradually increased, those for Among the other major price indices, the WPI fuel and manufactured products have remained consumer price index for industrial workers (CPI-IW) stable within a narrow range (Table A1-M3). reaches its seasonal peak in October, whereas the Industrial output, as measured by the index of consumer price indices for agricultural labourers (AL) industrial production (IIP), typically rises in March and rural labourers (RL) peak in November. All three and moderates in April, largely due to seasonal indices show seasonal easing in March. However, patterns in the manufacturing sector. Mining activity the seasonal fluctuations in CPI-IW, AL and RL were also peaks in March, with a seasonal low in August. Chart 1: Average3 Monthly Seasonal Factors within CPI a. All Commodities and Major Components (Per cent) 102 101 100 99 98 97 Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Food and beverages Clothing and footwear Housing rent Miscellaneous All commodities b. CPI-Food: Key Components c. CPI-Vegetables: Key Items (Per cent) (Per cent) 115 130 113 111 125 109 120 107 115 105 103 110 101 105 99 97 100 95 95 93 90 91 89 85 87 80 Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar 75 Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Cereals and products Fruits Vegetables Pulses and products Food and beverages Potato Onion Tomato Vegetables Sources: MOSPI; and Authors’ calculations. 3 Average of the seasonal factors of last 10 years. RBI Bulletin November 2025 105ARTICLE Seasonality in Key Economic Indicators of India Chart 2: Average Monthly Seasonal Factors of Chart 3: Average Monthly Seasonal Factors of WPI CPI-IW, AL and RL (Per cent) (Per cent) 103 101.5 102 101.0 100.5 101 100.0 100 99.5 99 99.0 98 98.5 97 98.0 Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar CPI-agricultural labourers CPI-rural labourers Manufactured products Fuel and power CPI-industrial workers Primary articles All commodities Sources: Labour Bureau; and Authors’ calculations. Sources: Office of the Economic Adviser; and Authors’ calculations. Electricity generation reaches its seasonal high in May Among the major sectors of the IIP, mining and declines in November. Within manufacturing, exhibited the highest seasonal fluctuation, followed seasonal peaks and troughs vary across subsectors. by electricity. Within the use-based classification, Food product manufacturing sees a peak in December, capital goods showed the most pronounced seasonal while beverage production reaches its seasonal high variation, followed by consumer non-durables in 2024- in May. The lowest production levels for food and 25. Among the eight core industries, coal production beverages occur in June and November, respectively. recorded the widest range of seasonal factors, while crude oil showed the least seasonal variation (Table Under the use-based classification of IIP, A1-M3). production of consumer durables peaks in October, driven by major Indian festivals, while non-durable Among high-frequency services sector goods production reaches its seasonal high in indicators, passenger vehicle sales (wholesale) reach December. Most other major categories, including a seasonal peak in October, driven by increased capital goods and infrastructure goods, experience demand during the festive season. Cargo and railway peak production in March. Seasonal troughs occur in traffic typically rise in March, while domestic air April for capital goods and consumer goods. Seasonal passenger traffic peaks in December and international trough of consumer durables and non-durables happen air travel sees the highest seasonal volume in during April and June, respectively. Primary goods January (Chart 5a and Table A1-M2). Most of these and intermediate goods show seasonal moderation indicators experience seasonal troughs in September, in September and February, respectively, while except for passenger vehicle sales, which decline infrastructure goods production softens in November. seasonally in December. In terms of the magnitude Among the eight core industries, most record seasonal of seasonal variation, passenger vehicle sales showed peaks in March, with the exceptions of fertilisers the widest range of seasonal factors in 2024–25, at and natural gas, which reach their seasonal highs in 27 percentage points. Railway and cargo traffic also October (Chart 4 and Table A1-M2). exhibited greater seasonal fluctuations compared 106 RBI Bulletin November 2025Seasonality in Key Economic Indicators of India ARTICLE Chart 4: Seasonal Factors for IIP and Eight Core Industries a. IIP b. Eight Core (Per cent) (Per cent) 124 142 120 138 134 116 130 112 126 108 122 118 104 114 100 110 96 106 102 92 98 88 94 84 90 Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar 80 Electricity Cement production Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Coal production Crude oil production Mining Manufacturing Natural gas production Fertiliser production Electricity General Index Petroleum refinery production Steel production Sources: MOSPI; Office of the Economic Adviser; and Authors’ calculations. to air passenger traffic over the past financial year M2). Seasonal fluctuations are more pronounced in (Table A1-M3). merchandise exports than in imports (Table A1-M3). Merchandise trade typically peaks in March, with Payment system indicators generally reach both exports and imports reaching seasonal highs. their seasonal peak in March, with the exception of Exports experience a seasonal low in November, while card payments. Real Time Gross Settlement (RTGS) imports moderate in February. Non-oil, non-gold, and transactions show a seasonal dip in February, while non-silver imports show a seasonal peak in December paper clearing hits its seasonal low in September. and a decline in February (Chart 5b and Table A1- Retail electronic payments (REC) decline in November Chart 5: Seasonal Factors for Services Sector and Merchandise Trade a. Services Sector Indices b. Merchandise Trade (Per cent) (Per cent) 113 111 109 107 105 103 101 99 97 95 93 91 89 87 85 Sources: DGCI&S; DGCA; Ministry of Railways; Indian Port Association; Society of Indian Automobile Manufacturers (SIAM); and Authors’ calculations. RBI Bulletin November 2025 107 rpA yaM nuJ luJ guA peS tcO voN ceD naJ beF raM 112 110 108 106 104 102 100 98 96 94 92 90 Cargo handled at major ports Passenger flown (km)-international Railway freight traffic Passenger vehicle sales(wholesale) Passenger flown (km)-domestic rpA yaM nuJ luJ guA peS tcO voN ceD naJ beF raM Exports Non-oil, non-gold and non-silver imports ImportsARTICLE Seasonality in Key Economic Indicators of India III.2. Seasonality in Quarterly Series4 Chart 6: Seasonal Factors for Payment System Indicators Quarterly estimates of gross domestic product (Per cent) 130 (GDP) and gross value added (GVA) typically show seasonal peaks in January to March quarter (Q4) 120 and troughs in July to September (Q2). Among GDP components, Government final consumption expenditure (GFCE) rises seasonally in April to June 110 (Q1) and eases in October to December (Q3). Gross fixed capital formation (GFCF) peaks in Q4 and dips 100 in Q2, aligning with the monsoon season. Private final consumption expenditure (PFCE) tends to increase in 90 Q3, driven by major festivals, following a seasonal Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar slowdown in Q2. On the supply side, agricultural Cards (value) Retail electronic clearing (value) Real time gross settlement (value) Paper clearing (value) output (value added) shows a seasonal dip in Q2 Sources: RBI; and Authors’ calculations. during the main kharif sowing period and peaks in Q3 (i.e., harvest time). Industrial output records and card payments experience seasonal moderation a seasonal high in Q4 and a low in Q3. In contrast, in February (Chart 6 and Table A1-M2). Among the services activity strengthens in Q2 but moderates in payment indicators selected for this study, REC Q3 (Chart 7 and Table A2-Q2). exhibited the highest seasonal variation, with seasonal factor range of 31.7 percentage points in 2024–25, The extent of seasonal variations, measured by followed by RTGS payments (range 30.1 percentage the range of seasonal factors, is higher in GDP than points) (Table A1-M3). GVA, possibly on account of the variations in net taxes, Chart 7: Seasonal Factors of National Accounts a. GDP and Components b. GVA and Components (Per cent) (Per cent) 110 140 120 105 100 100 80 95 60 90 40 85 20 80 0 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Real GDP Real PFCE Real GVA Agriculture Real GFCE Real GFCF Industry Services Sources: MOSPI; and Authors’ calculations. 4 In this article, the quarters correspond to the financial years i.e., Q1 corresponds to April to June, Q2 is from July to September, Q3 is October to December and Q4 is January to March. 108 RBI Bulletin November 2025Seasonality in Key Economic Indicators of India ARTICLE which influence market prices. GFCE exhibits the and Q1. Order book assessments and expectations highest seasonal variations among the expenditure- both peak in Q4. However, order book assessments side components of GDP. Seasonal fluctuations in bottom out in Q2, while expectations decline in Q1. GFCF and PFCE are approximately closer to each other. Manufacturers’ assessment of CU reaches its seasonal Seasonal fluctuations in GVA were higher in 2024-25 high in Q4, while expectations peak in Q3. Regarding than the last 10 years’ average. Among the supply pricing outlook, selling price assessments are higher side components, seasonal variations are highest in in Q1 and moderate in Q2, whereas expectations agriculture (Table A2-Q3). rise in Q1 but ease in Q4. Profitability assessments are strongest in Q4, while expectations for future CU of manufacturing companies, as measured profitability typically peak in Q2 (Chart 8 and Table by the OBICUS, typically peaks in Q4 and dips in A2-Q2). Q1. Seasonal variation in CU has remained relatively stable over the past decade, with an average range BAI and BEI indices exhibit relatively mild of 3.7 percentage points. The Business Assessment seasonal fluctuations, with seasonal factors averaging Index (BAI) and Business Expectation Index (BEI), between 1.8 and 2.3 percentage points in 2024-25. In derived from the IOS, show seasonal peaks in Q4 contrast, assessments related to production, capacity and Q3, respectively, while their troughs occur in Q2 utilisation and selling prices display more pronounced Chart 8: Seasonal Factors of IOS a. Overall Business b. Production c. Order Books (Per cent) (Per cent) (Per cent) 102 104 103 101 103 102 102 101 101 101 100 100 100 100 99 98 99 99 97 99 98 96 98 95 97 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 BAI BEI Assessment Expectation Assessment Expectation d. Selling Price e. Profit Margin (Per cent) (Per cent) 103 102 102 102 101 101 101 100 100 100 99 99 99 98 98 97 98 97 96 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Assessment Expectation Assessment Expectation Sources: RBI; and Authors’ calculations. RBI Bulletin November 2025 109ARTICLE Seasonality in Key Economic Indicators of India notable seasonal variation, with a ten-year average Chart 9: Seasonal Factors in Services Trade (Per cent) range of 6.1 percentage points. Overall, travel services 120 exports and imports display the most significant seasonal fluctuations within services trade (Table A2- 100 Q3). 80 III.3. Stability of Seasonality 60 The stability of seasonal variations is assessed using both parametric and non-parametric tests, with 40 the diagnostic results presented in Tables A1-M4 and A2-Q4. Additionally, the consistency of seasonal 20 factor estimates is evaluated by comparing the range 0 of seasonal factors for 2024-25 against their five-year Q1 Q2 Q3 Q4 averages from the pre-COVID period for both monthly Services exports Services exports as per ITES Travel services exports Travel services imports and quarterly series. Scatter plots indicate that Sources: RBI; and Authors’ calculations. seasonal variations during 2024-25 closely align with pre-pandemic averages across most monthly series seasonal variation (Table A2-Q3). (Chart 10a). A similar trend is observed in quarterly Overall services exports reach their seasonal peak series, except for one series in national accounts in Q4 and hit a trough in Q1. Within services exports, statistics (GFCE) and one series in balance of payments computer software and information technology (travel exports), which have shown increased seasonal enabled services (ITES) exports are seasonally strong variation recently (Chart 10b). in Q3, while travel services exports peak in Q4. On IV. Conclusion the import side, travel services see a seasonal high in Q1 (Chart 9 and Table A2-Q2). Services exports exhibit This article presents updated estimates of Chart 10: Range of Seasonal Variations in Monthly and Quarterly Series a. Monthly Series b. Quarterly Series Sources: Authors’ calculations. 110 RBI Bulletin November 2025 )tnec rep( 52-4202 fo egnar FS 60 40 20 0 0 20 40 60 SF range of average SF pre-COVID (per cent) Merchandise trade Payment system indicators Production indices Monetary and banking Price indices Services sector indices )tnec rep( 52-4202 fo egnar FS 60 40 20 0 0 20 40 60 SF range of average SF pre-COVID (per cent) Balance of payments National accounts statistics Industrial outlook survey OBICUSSeasonality in Key Economic Indicators of India ARTICLE seasonal factors for key economic indicators, revealing components, PFCE and GFCF trough in Q2, whereas that while overall seasonal patterns remain largely GFCE experiences seasonal low in Q3. Within supply stable, several indicators—such as cash in hand and side, GVA agriculture experiences the highest seasonal balances with the RBI, demand deposits, prices of variations. In IOS, manufacturers’ current assessment major vegetables, industrial production, passenger peaks during Q4 and expectations scales seasonal vehicle sales, merchandise exports and RTGS maximum in Q3. Services exports peak in Q4, while transactions—have experienced more pronounced exports of telecommunications, computer and seasonal fluctuations. Additionally, some indices and information services are strongest in Q3. banking and monetary aggregates have seen shifts in The pandemic caused major disruptions in their peak and trough months. economic activity, resulting in atypical data patterns. Banking indicators like bank credit, non-food Due to the limited post-pandemic data availability, credit and demand deposits typically reach their year- estimates of stochastic seasonality using the seasonal end peak in March. CPI experiences seasonal pressure ARIMA model may be affected. With this limited from July to November, primarily due to rising data, it is difficult to definitively identify changes in vegetable prices during the monsoon, while fruit prices the underlying data-generating process. The seasonal tend to peak in the summer. In industrial production, factor estimates presented in this article have been most items hit their highest levels in March, except derived with appropriate precautions and robustness consumer durables, which peak in October during the checks; however, these estimates may further evolve festive season. Both exports and imports also reach as more post-pandemic data become available. their seasonal highs in March, with exports showing References: more pronounced seasonal fluctuations than imports. Shiskin, J., Young, A. H., and Musgrave, J. C. (1967). Among the quarterly series, real GDP and GVA The X-11 variant of the census method II seasonal consistently peak in Q4, with seasonal variations adjustment program. U.S. Department of Commerce, in national account aggregates having increased Bureau of the Census. since the pandemic began, even after adjusting for pandemic-related volatility as detailed in the Gómez, Victor and Maravall, Agustin (1996). Programs technical annex. Both GDP and GVA reach their TRAMO and SEATS, Instruction for User (Beta Version: seasonal trough in Q2. Among the expenditure-side September 1996). Working Papers, Banco de España. RBI Bulletin November 2025 111ARTICLE Seasonality in Key Economic Indicators of India Technical Annex Seasonal patterns in economic data are systematic nature: additive outliers (AOs), level shifts (LSs), fluctuations that recur at specific times each year, temporary changes (TCs) and ramps. AOs affect only driven by factors such as weather, holidays and one observation in the whole series and hence, this cultural events. The X13-ARIMA-SEATS programme, effect is removed by a dummy variable, which takes developed by the US Census Bureau, is a widely used ‘0’ at break and ‘1’ for other period. LSs increases or tool for seasonal adjustment and trend extraction decreases all observations from a certain time point in time series analysis. This programme integrates onward by some constant amount, this LS effect is two methodologies: RegARIMA modeling, which removed by introducing a dummy variable which takes forecasts and models the underlying time series, value ‘-1’ for all the time point up-to the break point and SEATS (Signal Extraction in ARIMA Time Series), and ‘0’ for all the time points afterwards. TCs allow which decomposes the series into seasonal, trend and for an abrupt increase or decrease in the level of the irregular components. The decomposition process series that returns to its previous level exponentially, involves the iterative application of centered moving this effect is captured by a variable which takes value average filters, progressively refining the separation ‘0’ for all observation before the change point and of components. Various filters and moving averages (0< <1) thereafter. Ramps allow for a linear increase t α are employed throughout to accurately isolate the or decrease in the level of the series over a specified α different elements of the time series. time interval (say t - t). Ramps are smoothed out by 0 1 introducing a variable which take three values, ‘-1’ for The X-13 ARIMA-SEATS program incorporates time t<t, (t-t)/((t- t)–1) for t < t <t, and ‘0’ after two distinct seasonal adjustment modules. The 0 0 1 0 0 1 the time point t > t. first module employs the X-11 seasonal adjustment 1 methodology, as originally described by Shiskin, X-13 ARIMA employs Seasonal Autoregressive Young and Musgrave (1967). This module retains all Integrated Moving Average (SARIMA) models to the seasonal adjustment features found in the earlier identify and estimate the seasonal patterns within X-11 and X-11-ARIMA programmes, including the use economic time series. The selection of the SARIMA of traditional seasonal and trend moving averages as model order is guided by in-sample goodness-of- well as calendar and holiday adjustment procedures. fit measures, with the optimal model chosen based The second module utilises the ARIMA model-based on established information criteria. Consequently, seasonal adjustment approach from the SEATS the selected model provides a representation of the programme, developed by Victor Gomez and Agustin underlying data-generating process through averaged Maravall at the Bank of Spain. This version of X-13 parameter estimates. However, the onset of the ARIMA-SEATS fully integrates SEATS capabilities, COVID-19 pandemic introduced significant challenges providing stability and spectral diagnostics comparable to this modeling approach, complicating the to those available for X-11 adjustments. For the identification and stability of seasonal components. purposes of this article, the X-11-based seasonal The COVID-19 pandemic may have fundamentally adjustment module was employed to extract seasonal transformed the economic data-generating process factors. across numerous sectors. This shift has introduced X-13 ARIMA-SEATS provides four other types increased uncertainty and volatility into the data, of regression variables to deal with abrupt changes necessitating more adaptive and flexible analytical in the level of a series of a temporary or permanent approaches. 112 RBI Bulletin November 2025Seasonality in Key Economic Indicators of India ARTICLE The changes that occurred during the COVID In the X-13 ARIMA model, outlier adjustments period and the subsequent post-pandemic for the COVID period are implemented using three normalisation may have influenced seasonal patterns. types of outliers: AO, TC and LS. These outliers are However, the limited availability of data from the past automatically identified according to guidelines two to three years is insufficient to fully capture these established by the US Census Bureau, and their shifts using SARIMA models. To address potential significance is validated by correlating them with alterations in seasonality since COVID, the stability economic events in India. To ensure the stability of seasonal adjustments is maintained through of seasonal patterns, the range of seasonal factors outlier adjustments, comparisons with pre-pandemic from the most recent period is compared with the estimates and annual updates of seasonal factors for average range observed during the pre-pandemic the most recent periods. period. RBI Bulletin November 2025 113ARTICLE Seasonality in Key Economic Indicators of India Annex - I Table A1-M1: Time Period Used for Estimating Monthly Seasonal Factors Name of Sectors/Variables Time Period Name of Sectors/Variables Time Period Monetary and Banking Indicators (14 series) Index of Industrial Production (23 series) April 1994 to A.1.1 Broad Money (M3) E. IIP (Base 2011-12 = 100) General Index March 2025 A.1.1.1 Net Bank Credit to Government E.1.1 IIP - Primary goods (34.05%) A.1.1.2 Bank Credit to Commercial Sector E.1.2 IIP - Capital goods (8.22%) A.1.2 Narrow Money (M1) E.1.3 IIP - Intermediate goods (17.22%) April 2012 to A.1.3 Reserve Money (RM) E.1.4 IIP - Infrastructure/ construction goods (12.34%) March 2025 A.1.3.1 Currency in Circulation E.1.5 IIP - Consumer goods (28.17%) A.2.1 Aggregate Deposits (SCBs) April 1994 to E.1.5.1 IIP - Consumer durables (12.84%) A.2.1.1 Demand Deposits (SCBs) March 2025 E.1.5.2 IIP - Consumer non-durables (15.33%) A.2.1.2 Time Deposits (SCBs) E.2.1 IIP - Mining (14.37%) April 1994 to A.3.1 Cash in Hand and Balances with RBI (SCBs) E.2.2 IIP – Manufacturing (77.63%) March 2025 A.3.2 Bank Credit (SCBs) E.2.2.1 IIP - Manufacture of food products (5.30%) A.3.2.1 Loans, Cash Credits and Overdrafts (SCBs) E.2.2.2 IIP - Manufacture of beverages (1.04%) A.3.2.2 Non-Food Credit (SCBs) E.2.2.3 IIP - Manufacture of textiles (3.29%) April 2012 to A.3.3 Investments (SCBs) E.2.2.4 IIP - Manufacture of chemicals and chemical March 2025 products (7.87%) E.2.2.5 IIP - Manufacture of motor vehicles, trailers and Price Indices [CPI: 21 series and WPI: 8 series] semi-trailers (4.86%) April 1994 to B. CPI (Base: 2012 = 100) All Commodities E.2.3 IIP - Electricity (7.99%) March 2025 B.1 CPI - Food and beverages (45.86%) E.3 Cement Production (2.16%) B.1.1 CPI - Cereals and products (9.67%) E.4 Steel Production (7.22%) B.1.2 CPI - Meat and fish (3.61%) E.5 Coal Production (4.16%) April 2004 to B.1.3 CPI – Egg (0.43%) E.6 Crude Oil Production (3.62%) March 2025 B.1.4 CPI - Milk and products (6.61%) E.7 Petroleum Refinery Production (11.29%) B.1.5 CPI – Fruits (2.89%) E.8 Fertiliser Production (1.06%) B.1.6 CPI – Vegetables (6.04%) E.9 Natural Gas Production (2.77%) B.1.6.1 CPI – Potato (0.98%) January 2011 Service Sector Indicators (5 series) B.1.6.2 CPI – Onion (0.64%) to March 2025 F.1 Cargo handled at Major Ports B.1.6.3 CPI – Tomato (0.57%) F.2 Railway Freight Traffic April 1994 to B.1.7 CPI - Pulses and products (2.38%) F.3 Passenger flown (Km) - Domestic March 2025 B.1.8 CPI – Spices (2.50%) F.4 Passenger flown (Km) - International April 2004 to B.1.9 CPI - Non-alcoholic beverages (1.26%) F.5 Passenger Vehicle Sales (wholesale) March 2025 B.1.10 CPI - Prepared meals, snacks, sweets etc. (5.55%) Merchandise Trade (3 series) B.2 CPI - Clothing and footwear (6.53%) G.1 Exports April 1994 to B.3 CPI – Housing (10.07%) G.2 Imports March 2025 B.4 CPI – Miscellaneous (28.32%) G.3 Non-Oil, Non-Gold and Non-Silver Imports C.1 Consumer Price Index for Industrial Workers (Base: Payment System Indicators (4 Series) 2016=100) C.2 Consumer Price Index for Agricultural Labourers January 2000 April 2004 to H.1 Real Time Gross Settlement (Base: 1986-87=100) to March 2025 March 2025 C.3 Consumer Price Index for Rural Labourers (Base: 1986- April 2005 to H.2 Paper Clearing 87=100) March 2025 D. WPI (Base: 2011-12=100) All Commodities H.3 Retail Electronic Clearing (REC) April 2004 to D.1 WPI - Primary Articles (22.62%) H.4 Cards March 2025 D.1.1 WPI - Food Articles (15.26%) April 1994 to D.2 WPI - Fuel & Power (13.15%) March 2025 D.3 WPI – Manufactured Products (64.23%) D.3.1 WPI - Manufacture of Food Products (9.12%) D.3.2 WPI - Manufacture of Chemicals & Chemical April 2012 to Products (6.47%) March 2025 D.3.3 WPI - Manufacture of Basic Metals (9.65%) Note:The figures in brackets represent weights for groups, sub-groups and items under the respective general index. 114 RBI Bulletin November 2025Seasonality in Key Economic Indicators of India ARTICLE Table A1-M2: Average* Monthly Seasonal Factors of Select Economic Time Series (Per cent) SERIES NAME APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR 1 2 3 4 5 6 7 8 9 10 11 12 13 Monetary and Banking Indicators (14 series) A.1.1 Broad Money (M3) 101.0 100.6 100.3 100.3 99.9 99.7 99.7 99.3 99.4 99.5 99.7 100.5 A.1.1.1 Net Bank Credit to Government 101.0 100.4 100.0 101.3 101.2 99.7 99.5 100.2 98.4 99.3 99.7 99.5 A.1.1.2 Bank Credit to Commercial Sector 100.8 100.1 99.9 99.5 99.0 99.4 99.4 99.5 100.2 100.2 100.3 101.6 A.1.2 Narrow Money (M1) 101.8 101.3 101.1 99.7 99.0 99.0 98.6 98.3 98.9 99.0 100.1 103.2 A.1.3 Reserve Money (RM) 101.4 101.9 101.6 100.4 99.1 98.4 98.3 98.9 99.1 99.1 99.0 102.7 A.1.3.1 Currency in Circulation 102.6 102.5 101.9 100.2 99.1 98.0 98.1 98.6 98.7 99.3 100.0 100.8 A.2.1 Aggregate Deposits (SCBs) 100.6 100.2 100.0 100.1 99.9 100.3 99.9 99.6 99.8 99.7 99.5 100.4 A.2.1.1 Demand Deposits (SCBs) 100.8 98.7 99.9 98.4 97.7 102.9 98.8 98.9 100.4 98.6 98.8 106.0 A.2.1.2 Time Deposits (SCBs) 100.5 100.3 100.0 100.3 100.0 100.1 100.1 99.8 99.8 99.8 99.6 99.7 A.3.1 Cash in Hand and Balances with RBI (SCBs) 101.2 100.0 102.0 100.4 100.5 101.1 100.0 100.4 101.6 97.8 96.4 98.2 A.3.2 Bank Credit (SCBs) 100.6 100.0 99.9 99.4 99.0 99.9 99.6 99.6 100.3 100.1 100.1 101.4 A.3.2.1 Loans, Cash Credits and Overdrafts (SCBs) 100.5 100.0 99.9 99.4 99.0 99.9 99.7 99.7 100.3 100.2 100.1 101.3 A.3.2.2 Non-Food Credit (SCBs) 100.6 100.0 99.9 99.4 99.1 99.9 99.7 99.4 100.2 100.0 100.1 101.6 A.3.3 Investments (SCBs) 99.4 100.1 100.3 101.1 101.2 101.3 100.9 100.0 99.1 98.9 99.2 98.5 Price Indices [ CPI: 21 series and WPI: 8 series] B. CPI (Base: 2012 = 100) All Commodities 99.2 99.5 99.9 100.6 100.6 100.5 101.0 100.9 100.1 99.5 99.2 99.0 B.1 CPI - Food and beverages 98.3 98.9 100.0 101.0 101.3 101.2 101.9 101.8 100.4 99.0 98.2 98.0 B.1.1 CPI - Cereals and products 99.7 99.7 99.6 99.7 99.9 100.0 100.2 100.4 100.3 100.3 100.2 100.0 B.1.2 CPI - Meat and fish 99.5 101.2 103.0 102.1 100.1 99.9 99.8 99.2 98.7 99.1 98.7 98.7 B.1.3 CPI - Egg 96.3 96.6 99.0 100.4 98.5 98.4 99.4 101.9 104.2 104.8 102.1 98.7 B.1.4 CPI - Milk and products 99.8 100.0 100.0 100.1 100.1 100.1 100.1 100.1 100.0 99.9 99.9 99.8 B.1.5 CPI - Fruits 103.1 102.7 101.8 103.2 102.6 100.1 99.4 98.7 97.6 96.4 96.4 97.8 B.1.6 CPI - Vegetables 88.9 91.4 97.6 105.4 106.8 108.9 113.0 111.7 103.2 94.4 90.6 88.6 B.1.6.1 CPI - Potato 84.8 93.1 101.5 109.5 112.4 112.3 116.4 116.7 106.2 88.5 79.3 79.2 B.1.6.2 CPI - Onion 79.6 75.3 80.7 89.3 99.1 105.1 118.1 133.0 121.2 110.4 101.2 87.9 B.1.6.3 CPI - Tomato 75.4 83.2 104.7 136.5 121.5 112.1 118.2 120.8 98.4 83.5 74.4 71.8 B.1.7 CPI - Pulses and products 98.7 98.8 99.5 99.6 99.9 100.7 101.3 101.6 101.3 100.4 99.4 98.7 B.1.8 CPI - Spices 99.4 99.4 99.4 99.9 100.1 100.3 100.5 100.7 100.6 100.4 99.9 99.5 B.1.9 CPI - Non-alcoholic beverages 99.9 99.9 99.9 100.0 100.0 100.0 100.0 100.1 100.1 100.1 100.1 99.9 B.1.10 CPI - Prepared meals, snacks, sweets etc. 99.9 99.9 99.9 100.0 100.1 100.0 100.0 100.1 100.0 100.0 100.0 100.0 B.2 CPI - Clothing and footwear 99.9 99.9 100.0 100.0 100.0 100.0 100.1 100.1 100.1 100.0 99.9 99.9 B.3 CPI - Housing 100.4 100.2 99.3 99.6 100.0 100.0 100.4 100.4 99.6 100.0 100.2 100.0 B.4 CPI - Miscellaneous 99.9 99.9 99.9 100.2 100.2 100.2 100.1 100.0 99.9 99.9 99.9 99.8 C.1 Consumer Price Index for Industrial Workers 99.4 99.5 99.9 100.8 100.6 100.5 100.9 100.7 99.9 99.7 99.1 99.1 (Base: 2016=100) C.2 Consumer Price Index for Agricultural Labourers 99.2 99.4 99.7 100.0 100.4 100.4 100.9 101.0 100.6 100.0 99.4 99.1 (Base: 1986-87=100) C.3 Consumer Price Index for Rural Labourers (Base: 99.2 99.4 99.7 100.0 100.4 100.4 100.8 100.9 100.5 100.0 99.4 99.1 1986-87=100) D. WPI (Base: 2011-12=100) All Commodities 99.9 100.0 100.0 100.4 100.2 100.2 100.5 100.7 99.8 99.5 99.5 99.5 D.1 WPI – Primary Articles 99.2 99.2 100.1 101.2 101.3 100.8 102.0 102.2 99.8 98.5 98.1 97.6 D.1.1 WPI - Food Articles 98.6 98.8 100.2 101.6 101.4 101.5 103.1 103.0 99.7 98.1 97.1 96.7 D.2 WPI – Fuel & Power 99.4 100.5 99.8 100.5 99.4 99.5 100.1 100.6 99.9 100.2 100.5 99.8 D.3 WPI – Manufactured Products 100.4 100.4 100.2 100.0 99.9 99.9 100.0 99.8 99.6 99.9 99.9 100.1 D.3.1 WPI - Manufacture of Food Products 100.3 100.1 100.2 100.0 100.4 100.4 100.1 100.1 99.8 99.7 99.4 99.6 D.3.2 WPI - Manufacture of Chemicals & Chemical 100.3 100.5 100.3 100.1 100.0 99.8 99.9 99.8 99.6 99.7 99.9 100.2 Products D.3.3 WPI - Manufacture of Basic Metals 101.1 101.6 100.8 99.4 99.0 99.5 99.8 99.1 98.9 99.8 100.2 100.7 RBI Bulletin November 2025 115ARTICLE Seasonality in Key Economic Indicators of India SERIES NAME APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR 1 2 3 4 5 6 7 8 9 10 11 12 13 Index of Industrial Production (23 series) E. IIP (Base 2011-12 = 100) General Index 96.8 100.9 98.8 97.5 97.1 97.5 99.8 98.1 103.0 103.4 98.3 108.6 E.1.1 IIP - Primary goods 98.4 103.5 100.5 98.7 96.4 94.1 97.9 97.2 102.1 103.8 97.0 110.2 E.1.2 IIP - Capital goods 89.3 97.4 100.8 96.5 96.3 102.6 98.7 96.5 100.4 100.8 100.9 119.3 E.1.3 IIP - Intermediate goods 97.6 100.4 98.3 101.3 99.7 98.4 99.1 97.5 102.3 102.2 96.6 106.5 E.1.4 IIP - Infrastructure/ construction goods 99.2 102.2 100.2 97.5 97.4 96.0 99.5 94.4 102.0 104.0 99.7 108.6 E.1.5 IIP - Consumer goods 95.2 98.8 95.4 98.5 97.5 100.9 99.5 101.4 105.1 103.5 99.2 105.0 E.1.5.1 IIP - Consumer durables 95.7 100.0 98.3 101.0 99.9 105.8 106.6 97.8 96.5 98.1 95.8 104.1 E.1.5.2 IIP - Consumer non-durables 95.2 97.6 94.7 96.9 95.6 96.8 97.1 103.3 110.2 106.7 100.9 104.8 E.2.1 IIP - Mining 97.7 101.1 96.0 89.7 86.2 86.3 96.4 101.1 107.7 110.4 105.3 122.3 E.2.2 IIP - Manufacturing 95.8 100.4 98.3 98.9 97.9 98.6 99.4 98.7 103.1 103.0 98.1 107.6 E.2.2.1 IIP - Manufacture of food products 95.7 89.0 86.6 90.6 90.0 89.4 93.9 105.8 121.0 118.7 110.6 108.1 E.2.2.2 IIP - Manufacture of beverages 111.1 120.4 107.4 93.6 89.2 91.2 89.9 88.8 93.0 98.2 100.9 117.1 E.2.2.3 IIP - Manufacture of textiles 98.3 99.1 97.4 100.1 100.6 100.6 101.1 99.3 103.2 101.7 96.7 102.0 E.2.2.4 IIP - Manufacture of chemicals and chemical 95.5 101.3 100.2 104.0 101.4 100.7 100.7 97.1 100.9 101.0 93.8 103.1 products E.2.2.5 IIP - Manufacture of motor vehicles, trailers 98.0 100.6 97.2 100.9 98.5 100.2 102.8 99.5 93.5 101.6 100.3 106.9 and semi-trailers E.2.3 IIP - Electricity 101.2 107.8 104.8 105.0 104.2 101.4 100.8 90.3 94.6 96.6 90.7 101.7 E.3 Cement Production 103.4 102.1 103.5 94.3 90.6 90.4 98.2 92.5 102.2 104.9 101.8 116.5 E.4 Steel Production 98.2 102.0 99.1 97.7 98.9 97.4 99.7 96.2 101.9 103.7 98.0 107.4 E.5 Coal Production 93.1 95.1 90.4 82.4 79.2 80.0 93.7 102.9 111.7 116.9 114.2 140.1 E.6 Crude Oil Production 98.9 102.5 99.1 101.8 101.3 97.7 101.5 98.3 101.7 102.0 92.3 102.9 E.7 Petroleum Refinery Production 97.7 101.5 99.0 100.8 97.0 93.4 100.3 100.4 103.7 104.7 95.2 106.6 E.8 Fertiliser Production 84.0 98.1 100.8 103.3 104.8 102.7 105.5 103.2 104.6 103.8 94.2 94.3 E.9 Natural Gas Production 96.6 100.6 98.8 102.3 102.3 99.2 102.9 99.6 102.1 102.6 91.6 101.4 Service Sector Indicators (5 series) F.1 Cargo handled at Major Ports 100.4 102.7 97.9 98.9 97.0 92.9 98.1 98.6 103.0 104.7 96.4 109.9 F.2 Railway Freight Traffic 97.6 102.1 98.5 97.3 95.4 93.2 97.7 98.0 103.4 105.5 97.6 113.4 F.3 Passenger flown (Km) - Domestic 99.7 106.0 98.3 96.0 96.1 93.5 99.4 101.1 107.2 103.9 98.2 101.0 F.4 Passenger flown (Km) - International 94.1 98.5 98.1 102.2 101.9 93.2 95.4 98.1 108.3 110.7 97.0 102.6 F.5 Passenger Vehicle Sales (wholesale) 96.4 93.7 90.6 99.4 98.5 105.0 114.0 100.1 89.7 105.0 101.4 106.0 Merchandise Trade (3 series) G.1 Exports 98.1 102.7 97.7 97.9 96.6 98.8 95.4 94.7 103.7 98.2 99.8 116.5 G.2 Imports 95.1 102.5 98.3 100.1 101.5 98.9 103.2 99.9 104.1 97.7 93.5 105.2 G.3 Non-Oil, Non-Gold and Non-Silver Imports 96.0 99.7 101.6 103.5 100.4 102.9 101.3 97.5 104.1 99.1 91.3 102.6 Payment System Indicators (4 series) H.1 RTGS 93.9 95.9 103.0 98.0 93.5 101.8 96.7 93.1 107.6 98.9 91.7 126.0 H.2 Paper Clearing 107.8 102.0 95.3 100.5 94.8 94.4 100.0 94.6 101.5 97.8 95.6 115.5 H.3 REC 96.5 96.4 100.3 98.5 96.4 98.7 101.0 92.8 103.2 97.3 93.0 126.7 H.4 Cards 99.5 103.2 99.3 103.1 101.3 95.8 109.8 98.1 102.0 99.9 88.3 100.1 *: Average of last ten years’ monthly seasonal factors, in general. Here, the average monthly seasonal factors have been computed on the basis of last 10 years (i.e., April 2015 to March 2025). Numbers marked in ‘bold’ are peaks and troughs of respective series. 116 RBI Bulletin November 2025Seasonality in Key Economic Indicators of India ARTICLE Table A1-M3: Range (Difference Between Peak and Trough) of Monthly Seasonal Factors (Percentage points) 2015- 2016- 2017- 2018- 2019- 2020- 2021- 2022- 2023- 2024- Average SERIES \ YEAR 16 17 18 19 20 21 22 23 24 25 Range 1 2 3 4 5 6 7 8 9 10 11 12 Monetary and Banking Indicators (14 series) A.1.1 Broad Money (M3) 2.0 2.1 2.2 2.1 1.9 1.8 1.6 1.5 1.3 1.3 1.7 A.1.1.1 Net Bank Credit to Government 3.6 3.6 3.5 3.4 3.1 2.7 2.5 2.6 3.1 3.2 2.9 A.1.1.2 Bank Credit to Commercial Sector 2.9 3.1 3.2 3.2 3.0 2.8 2.4 2.1 1.8 1.7 2.6 A.1.2 Narrow Money (M1) 4.5 5.3 5.9 6.2 6.0 5.4 4.7 4.2 3.9 3.7 4.9 A.1.3 Reserve Money (RM) 4.7 4.8 4.7 4.7 4.6 4.5 4.3 4.1 4.0 3.9 4.4 A.1.3.1 Currency in Circulation 5.0 5.1 5.1 5.0 4.6 4.3 4.3 4.5 4.6 4.7 4.6 A.2.1 Aggregate Deposits (SCBs) 1.1 1.4 1.7 1.7 1.5 1.2 1.1 1.1 1.1 1.1 1.1 A.2.1.1 Demand Deposits (SCBs) 7.4 9.5 11.5 12.2 11.3 9.4 7.4 5.9 5.1 5.5 8.4 A.2.1.2 Time Deposits (SCBs) 1.1 1.0 1.0 1.0 1.1 1.2 1.1 1.1 1.1 1.0 1.0 A.3.1 Cash in Hand and Balances with RBI (SCBs) 4.9 5.4 5.6 6.2 7.1 7.4 7.1 6.7 7.1 8.0 5.6 A.3.2 Bank Credit (SCBs) 2.8 3.2 3.4 3.5 3.2 2.7 2.0 1.5 1.2 1.1 2.4 A.3.2.1 Loans, Cash Credits and Overdrafts (SCBs) 2.7 3.0 3.2 3.3 3.0 2.5 1.9 1.4 1.2 1.1 2.3 A.3.2.2 Non-Food Credit (SCBs) 3.0 3.5 3.8 3.9 3.5 2.8 2.1 1.3 0.9 0.9 2.5 A.3.3 Investments (SCBs) 3.7 3.6 3.5 3.3 3.2 3.1 3.0 2.9 2.6 2.4 2.8 Price Indices [ CPI: 21 series and WPI: 8 series] B. CPI (Base: 2012 = 100) All Commodities 1.9 1.9 1.8 1.9 2.0 2.0 2.1 2.1 2.2 2.2 2.0 B.1 CPI - Food and beverages 3.8 3.7 3.6 3.7 3.8 4.0 4.0 4.2 4.3 4.4 3.9 B.1.1 CPI - Cereals and products 0.7 0.7 0.6 0.6 0.6 0.7 0.8 1.0 1.2 1.3 0.8 B.1.2 CPI - Meat and fish 3.2 3.2 3.4 3.7 4.1 4.5 5.0 5.5 5.9 6.1 4.3 B.1.3 CPI - Egg 7.0 6.8 6.6 6.7 7.1 7.9 9.1 10.3 11.3 12.0 8.5 B.1.4 CPI - Milk and products 0.6 0.5 0.4 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.3 B.1.5 CPI - Fruits 6.2 6.1 6.2 6.5 6.7 7.2 7.4 7.6 7.7 7.8 6.8 B.1.6 CPI - Vegetables 22.5 22.1 21.9 22.7 23.8 24.8 26.0 26.8 27.6 27.9 24.4 B.1.6.1 CPI - Potato 36.5 35.3 34.7 35.6 37.5 38.7 40.6 41.1 41.3 40.5 37.4 B.1.6.2 CPI - Onion 40.4 43.0 48.6 55.9 62.6 66.4 67.8 66.2 64.1 62.2 57.7 B.1.6.3 CPI - Tomato 61.9 60.9 61.0 61.4 63.3 65.2 66.6 67.2 68.9 71.4 64.7 B.1.7 CPI - Pulses and products 3.4 3.4 3.1 2.7 2.3 2.2 2.8 3.6 4.3 4.7 3.0 B.1.8 CPI - Spices 1.1 1.1 1.0 0.9 0.7 0.9 1.3 1.8 2.4 2.8 1.3 B.1.9 CPI - Non-alcoholic beverages 0.3 0.2 0.2 0.3 0.3 0.3 0.3 0.3 0.2 0.2 0.2 B.1.10 CPI - Prepared meals, snacks, sweets etc. 0.5 0.4 0.4 0.3 0.2 0.1 0.1 0.1 0.1 0.1 0.2 B.2 CPI - Clothing and footwear 0.5 0.4 0.3 0.3 0.3 0.2 0.2 0.1 0.1 0.2 0.2 B.3 CPI - Housing 1.1 1.1 1.2 1.2 1.1 1.1 1.1 1.0 1.0 1.0 1.1 B.4 CPI - Miscellaneous 0.5 0.5 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.3 0.4 C.1 Consumer Price Index for Industrial Workers 2.3 2.2 2.0 1.8 1.6 1.7 1.8 1.8 1.9 1.9 1.8 (Base: 2016=100) C.2 Consumer Price Index for Agricultural Labourers 2.4 2.2 2.0 1.9 1.9 1.8 1.8 1.9 1.8 1.9 2.0 (Base: 1986-87=100) C.3 Consumer Price Index for Rural Labourers (Base: 2.2 2.0 1.9 1.8 1.7 1.7 1.7 1.8 1.7 1.7 1.8 1986-87=100) D. WPI (Base: 2011-12=100) All Commodities 1.5 1.4 1.3 1.2 1.2 1.3 1.3 1.3 1.3 1.3 1.2 D.1 WPI – Primary Articles 5.0 4.8 4.7 4.8 4.8 4.7 4.5 4.4 4.4 4.6 4.5 D.1.1 WPI - Food Articles 5.6 5.5 5.8 6.4 6.7 7.0 7.1 7.2 7.3 7.3 6.5 D.2 WPI – Fuel & Power 3.0 2.6 2.0 1.6 1.2 1.5 1.8 1.9 2.0 2.1 1.2 D.3 WPI – Manufactured Products 0.7 0.6 0.6 0.6 0.7 0.9 1.0 1.1 1.0 1.0 0.8 D.3.1 WPI - Manufacture of Food Products 1.5 1.3 1.2 1.1 1.1 1.1 1.2 1.3 1.2 1.1 1.0 D.3.2 WPI - Manufacture of Chemicals & Chemical 0.8 0.8 0.9 0.9 1.0 1.1 1.2 1.1 0.9 0.7 0.9 Products D.3.3 WPI - Manufacture of Basic Metals 1.8 2.1 2.3 2.6 2.8 3.1 3.3 3.3 3.2 3.0 2.7 RBI Bulletin November 2025 117ARTICLE Seasonality in Key Economic Indicators of India 2015- 2016- 2017- 2018- 2019- 2020- 2021- 2022- 2023- 2024- Average SERIES \ YEAR 16 17 18 19 20 21 22 23 24 25 Range 1 2 3 4 5 6 7 8 9 10 11 12 Index of Industrial Production (23 series) E. IIP (Base 2011-12 = 100) General Index 12.8 13.0 13.2 13.1 12.8 12.0 11.5 11.2 11.2 11.2 11.9 E.1.1 IIP - Primary goods 13.8 14.2 15.1 16.0 16.7 17.1 17.4 17.3 17.1 16.8 16.2 E.1.2 IIP - Capital goods 34.5 32.4 31.0 29.8 29.2 28.7 28.4 28.4 28.6 29.0 30.0 E.1.3 IIP - Intermediate goods 10.7 10.7 10.4 10.4 10.3 10.0 9.7 9.5 9.4 9.3 10.0 E.1.4 IIP - Infrastructure/ construction goods 12.8 13.4 13.8 14.2 14.2 14.4 14.6 14.8 14.9 15.0 14.2 E.1.5 IIP - Consumer goods 10.5 10.0 9.9 10.5 11.1 11.0 11.3 11.3 11.0 10.7 9.9 E.1.5.1 IIP - Consumer durables 12.7 11.7 10.9 11.1 11.2 11.0 10.8 10.7 10.9 10.7 10.9 E.1.5.2 IIP - Consumer non-durables 13.1 13.6 14.3 14.9 15.5 16.2 16.8 17.3 18.1 18.7 15.5 E.2.1 IIP - Mining 32.0 33.0 34.8 36.4 37.8 38.3 38.3 38.0 37.3 36.6 36.1 E.2.2 IIP - Manufacturing 12.6 12.6 12.8 12.8 12.6 12.1 11.4 10.7 10.1 9.8 11.7 E.2.2.1 IIP - Manufacture of food products 36.1 36.6 36.8 36.4 35.4 34.6 33.1 32.2 31.6 32.3 34.5 E.2.2.2 IIP - Manufacture of beverages 46.0 39.6 34.8 31.3 29.2 28.8 27.4 27.2 28.4 30.1 31.6 E.2.2.3 IIP - Manufacture of textiles 6.4 5.3 5.3 5.8 6.6 7.1 7.3 7.5 7.6 7.6 6.5 E.2.2.4 IIP - Manufacture of chemicals and chemical 10.8 11.4 11.7 11.4 10.6 10.8 10.5 10.2 9.8 9.4 10.2 products E.2.2.5 IIP - Manufacture of motor vehicles, trailers and 14.8 15.2 15.3 14.6 13.7 12.5 12.0 11.7 11.8 12.6 13.4 semi-trailers E.2.3 IIP - Electricity 14.6 15.8 16.9 17.8 18.3 19.6 20.6 20.8 20.7 20.4 17.5 E.3 Cement Production 22.4 23.0 24.3 26.1 27.8 28.9 29.0 28.6 27.9 27.5 26.1 E.4 Steel Production 9.4 9.9 10.7 11.6 12.0 12.0 11.9 11.8 11.6 11.4 11.2 E.5 Coal Production 56.3 58.7 61.8 64.0 64.7 64.0 62.5 60.4 58.9 58.0 60.9 E.6 Crude Oil Production 10.5 10.6 10.7 10.6 10.6 10.7 10.7 10.7 10.6 10.4 10.6 E.7 Petroleum Refinery Production 9.8 10.1 11.0 12.6 14.1 15.4 15.8 15.2 14.2 13.4 13.1 E.8 Fertiliser Production 24.5 22.7 22.0 21.8 22.1 22.2 21.5 20.7 19.2 18.5 21.4 E.9 Natural Gas Production 10.8 10.9 11.0 11.2 11.6 11.9 11.9 11.9 11.6 11.3 11.4 Service Sector Indicators (5 series) F.1 Cargo handled at Major Ports 15.5 15.8 16.3 16.9 17.3 17.4 17.6 17.8 17.8 17.8 17.0 F.2 Railway Freight Traffic 18.1 18.3 19.1 19.7 20.1 20.4 20.9 21.4 21.9 22.3 20.2 F.3 Passenger flown (Km) - Domestic 16.7 14.7 13.1 12.7 13.5 14.2 14.6 14.6 14.4 14.0 13.8 F.4 Passenger flown (Km) - International 20.0 20.2 20.7 20.3 19.5 18.4 17.6 16.5 15.6 15.5 17.6 F.5 Passenger Vehicle Sales (wholesale) 19.3 20.9 22.4 25.1 26.8 27.3 26.4 26.1 26.7 27.0 24.3 Merchandise Trade (3 series) G.1 Exports 18.6 18.8 20.3 21.7 22.7 22.8 23.2 23.1 23.4 23.9 21.8 G.2 Imports 13.4 13.3 13.0 12.6 12.7 11.9 11.1 11.2 13.3 14.4 11.7 G.3 Non-Oil, Non-Gold and Non-Silver Imports 13.2 13.4 13.3 13.1 13.0 12.9 12.6 12.3 12.3 12.9 12.8 Payment System Indicators (4 series) H.1 RTGS 42.5 40.2 38.1 36.2 34.2 32.0 31.3 30.8 30.2 30.1 34.3 H.2 Paper Clearing 21.8 21.2 21.3 21.4 21.2 21.5 21.9 21.8 21.8 22.1 21.1 H.3 REC 35.0 35.4 35.6 35.5 35.3 34.7 33.6 32.7 32.0 31.7 33.9 H.4 Cards 20.1 20.4 21.1 21.6 22.3 22.4 22.4 22.1 21.7 21.4 21.5 Note: Average seasonal factor range is the range of average seasonal factors for last ten years; range is calculated as the difference between maximum and minimum of monthly seasonal factors. 118 RBI Bulletin November 2025Seasonality in Key Economic Indicators of India ARTICLE Table A1-M4: Major Diagnostics of all the Monthly Indicators Seasonality in Original Series Residual Seasonality Quality diagnostics Name of variable F test KW test F test F test 3 yr M7 Q p-value p-value p-value p-value A.1.1 Broad Money (M3) 0.00 0.00 1.00 0.93 0.31 0.26 A.1.1.1 Net Bank Credit to Government 0.00 0.00 0.97 0.40 0.39 0.36 A.1.1.2 Bank Credit to Commercial Sector 0.00 0.00 1.00 0.39 0.35 0.27 A.1.2 Narrow Money (M1) 0.00 0.00 0.96 0.64 0.28 0.27 A.1.3 Reserve Money (RM) 0.00 0.00 0.46 0.91 0.28 0.22 A.1.3.1 Currency in Circulation 0.00 0.00 0.50 0.29 0.20 0.30 A.2.1 Aggregate Deposits (SCBs) 0.00 0.00 1.00 0.96 0.55 0.41 A.2.1.1 Demand Deposits (SCBs) 0.00 0.00 0.84 0.62 0.50 0.60 A.2.1.2 Time Deposits (SCBs) 0.00 0.00 1.00 0.95 0.57 0.32 A.3.1 Cash in Hand and Balances with RBI (SCBs) 0.00 0.00 0.97 0.67 1.31 0.92 A.3.2 Bank Credit (SCBs) 0.00 0.00 1.00 0.97 0.47 0.31 A.3.2.1 Loans, Cash Credits and Overdrafts (SCBs) 0.00 0.00 1.00 0.96 0.46 0.32 A.3.2.2 Non-Food Credit (SCBs) 0.00 0.00 1.00 0.97 0.72 0.45 A.3.3 Investments (SCBs) 0.00 0.00 0.88 0.99 0.44 0.31 B. CPI (Base: 2012 = 100) All Commodities 0.00 0.00 1.00 0.88 0.27 0.31 B.1 CPI - Food and beverages 0.00 0.00 1.00 0.80 0.23 0.32 B.1.1 CPI - Cereals and products 0.00 0.00 1.00 0.60 0.93 0.62 B.1.2 CPI - Meat and fish 0.00 0.00 0.94 0.82 0.43 0.44 B.1.3 CPI - Egg 0.00 0.00 1.00 0.62 0.39 0.35 B.1.4 CPI - Milk and products 0.00 0.00 0.97 1.00 1.27 0.57 B.1.5 CPI - Fruits 0.00 0.00 0.99 0.98 0.25 0.27 B.1.6 CPI - Vegetables 0.00 0.00 0.99 0.66 0.24 0.29 B.1.6.1 CPI - Potato 0.00 0.00 1.00 0.93 0.22 0.32 B.1.6.2 CPI - Onion 0.00 0.00 0.90 0.97 0.41 0.40 B.1.6.3 CPI - Tomato 0.00 0.00 0.93 0.88 0.36 0.68 B.1.7 CPI - Pulses and products 0.00 0.00 0.98 0.92 0.67 0.53 B.1.8 CPI - Spices 0.00 0.00 1.00 1.00 1.25 0.83 B.1.9 CPI - Non-alcoholic beverages 0.00 0.00 1.00 1.00 1.08 0.53 B.1 .10 CPI - Prepared meals, snacks, sweets etc. 0.00 0.00 0.97 1.00 1.56 0.69 B.2 CPI - Clothing and footwear 0.00 0.00 0.97 1.00 1.34 0.67 B.3 CPI - Housing 0.00 0.00 0.94 1.00 0.38 0.42 B.4 CPI - Miscellaneous 0.00 0.00 1.00 0.97 0.99 0.47 C.1 Consumer Price Index for Industrial Workers 0.00 0.00 1.00 0.96 0.26 0.29 (Base: 2016=100) C.2 Consumer Price Index for Agricultural Labourers 0.00 0.00 1.00 0.99 0.25 0.29 (Base: 1986-87=100) C.3 Consumer Price Index for Rural Labourers 0.00 0.00 1.00 0.99 0.26 0.26 (Base: 1986-87=100) D. WPI (Base: 2011-12=100) All Commodities 0.00 0.00 1.00 0.93 0.46 0.42 D.1 WPI – Primary Articles 0.00 0.00 0.99 0.48 0.31 0.37 D.1.1 WPI - Food Articles 0.00 0.00 0.86 0.52 0.28 0.31 D.2 WPI – Fuel & Power 0.00 0.00 1.00 1.00 1.62 0.77 D.3 WPI – Manufactured Products 0.00 0.00 1.00 0.97 0.67 0.49 D.3.1 WPI - Manufacture of Food Products 0.00 0.00 1.00 0.99 0.98 0.65 D.3.2 WPI - Manufacture of Chemicals & Chemical Products 0.00 0.00 1.00 1.00 1.39 0.65 D.3.3 WPI - Manufacture of Basic Metals 0.00 0.00 1.00 0.82 0.87 0.61 D.3.4 WPI - Manufacture of Machinery and Equipment 0.02 0.01 1.00 0.98 1.72 0.80 RBI Bulletin November 2025 119ARTICLE Seasonality in Key Economic Indicators of India Seasonality in Original Series Residual Seasonality Quality diagnostics Name of variable F test KW test F test F test 3 yr M7 Q p-value p-value p-value p-value E. IIP (Base 2011-12 = 100) General Index 0.00 0.00 0.21 0.68 0.16 0.23 E.1.1 IIP - Primary goods 0.00 0.00 0.21 0.79 0.26 0.74 E.1.2 IIP - Capital goods 0.00 0.00 0.27 0.92 0.29 0.48 E.1.3 IIP - Intermediate goods 0.00 0.00 0.33 0.24 0.34 0.39 E.1.4 IIP - Infrastructure/ construction goods 0.00 0.00 0.28 0.99 0.39 0.46 E.1.5 IIP - Consumer goods 0.00 0.00 0.40 0.18 0.46 0.60 E.1.5.1 IIP - Consumer durables 0.00 0.00 0.13 0.57 0.38 0.43 E.1.5.2 IIP - Consumer non-durables 0.00 0.00 0.39 0.19 0.38 0.77 E.2.1 IIP - Mining 0.00 0.00 0.87 0.33 0.23 0.36 E.2.2 IIP - Manufacturing 0.00 0.00 0.14 0.55 0.22 0.27 E.2.2.1 IIP - Manufacture of food products 0.00 0.00 1.00 0.98 0.17 0.47 E.2.2.2 IIP - Manufacture of beverages 0.00 0.00 0.43 0.64 0.49 0.43 E.2.2.3 IIP - Manufacture of textiles 0.00 0.00 0.34 0.99 0.58 0.61 E.2.2.4 IIP - Manufacture of chemicals and chemical products 0.00 0.00 0.49 0.79 0.49 0.79 E.2.2.5 IIP - Manufacture of motor vehicles, trailers and 0.00 0.00 0.30 0.34 0.57 0.62 semi-trailers E.2.3 IIP - Electricity 0.00 0.00 0.47 0.99 0.52 0.53 E.3 Cement Production 0.00 0.00 0.40 0.83 0.22 0.31 E.4 Steel Production 0.00 0.00 0.52 0.87 0.47 0.63 E.5 Coal Production 0.00 0.00 0.62 0.76 0.12 0.32 E.6 Crude Oil Production 0.00 0.00 0.89 1.00 0.19 0.32 E.7 Petroleum Refinery Production 0.00 0.00 0.97 0.29 0.44 0.68 E.8 Fertiliser Production 0.00 0.00 0.75 0.11 0.29 0.61 E.9 Natural Gas Production 0.00 0.00 0.82 0.94 0.23 0.27 F.1 Cargo handled at Major Ports 0.00 0.00 0.94 0.99 0.29 0.50 F.2 Railway Freight Traffic 0.00 0.00 0.29 0.95 0.13 0.33 F.3 Passenger flown (Km) - Domestic 0.00 0.00 0.36 0.41 0.29 0.32 F.4 Passenger flown (Km) - International 0.00 0.00 0.70 0.23 0.37 0.50 F.5 Passenger Vehicle Sales (wholesale) 0.00 0.00 0.77 0.07 0.39 0.41 G.1 Exports 0.00 0.00 0.56 0.88 0.36 0.54 G.2 Imports 0.00 0.00 0.99 0.86 0.80 0.81 G.3 Non-Oil, Non-Gold and Non-Silver Imports 0.00 0.00 1.00 0.24 0.56 0.70 H.1 RTGS 0.00 0.00 0.40 0.52 0.42 0.46 H.2 Paper Clearing 0.00 0.00 0.14 0.76 0.27 0.70 H.3. REC 0.00 0.00 0.10 0.83 0.39 0.29 H.4 Cards 0.00 0.00 0.04 0.95 0.36 0.37 Notes: 1. Test for seasonality in original series: F-test for the presence of seasonality assuming stability and Kruskall and Wallis test (a nonparametric test for stable seasonality). 2. Test for seasonality in seasonally adjusted series: F-test for the presence of seasonality assuming stability for full sample and for latest 3 years. 3. M7 corresponds to the amount of moving seasonality present relative to the amount of stable seasonality (acceptable range is between 0 and 1). However, M Diagnostics are aggregated in a single quality control indicator - Q, which gives the overall assessment of the adjustment (acceptable range is between 0 and 1). 120 RBI Bulletin November 2025Seasonality in Key Economic Indicators of India ARTICLE Table A1-M5: Monthly Seasonal Factors of Select Economic Time Series for 2024-25 (Per cent) SERIES NAME APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR 1 2 3 4 5 6 7 8 9 10 11 12 13 Monetary and Banking Indicators (14 series) A.1.1 Broad Money (M3) 100.7 100.6 100.5 100.4 99.8 99.4 99.7 99.5 100.0 99.6 99.7 100.1 A.1.1.1 Net Bank Credit to Government 101.8 99.6 99.5 100.6 100.5 99.2 99.6 101.2 98.6 98.6 99.9 101.0 A.1.1.2 Bank Credit to Commercial Sector 100.7 100.1 100.0 99.4 99.2 99.3 99.5 99.8 100.5 100.3 100.2 100.8 A.1.2 Narrow Money (M1) 101.6 101.4 101.8 99.7 98.6 98.3 98.4 98.8 100.0 99.4 99.9 102.0 A.1.3 Reserve Money (RM) 101.1 102.3 101.8 100.7 99.2 98.4 98.3 98.9 98.9 99.1 99.4 101.9 A.1.3.1 Currency in Circulation 102.5 102.4 101.8 100.3 99.2 98.2 97.9 98.6 98.5 99.3 100.1 101.2 A.2.1 Aggregate Deposits (SCBs) 100.5 100.3 100.2 100.1 100.2 100.5 99.8 99.7 100.0 99.7 99.6 99.4 A.2.1.1 Demand Deposits (SCBs) 101.5 99.1 100.4 98.6 97.3 102.8 99.1 99.2 101.0 99.3 99.4 101.9 A.2.1.2 Time Deposits (SCBs) 100.3 100.3 100.2 100.3 100.2 100.3 100.0 99.8 100.1 99.7 99.6 99.3 A.3.1 Cash in Hand and Balances with RBI (SCBs) 102.1 99.5 101.0 101.2 101.6 103.1 101.5 101.4 99.8 97.9 96.0 95.1 A.3.2 Bank Credit (SCBs) 100.3 99.9 99.7 99.6 99.4 100.1 99.9 100.0 100.5 100.2 100.1 100.2 A.3.2.1 Loans, Cash Credits and Overdrafts (SCBs) 100.2 99.8 99.7 99.5 99.4 100.1 99.9 100.1 100.5 100.3 100.1 100.2 A.3.2.2 Non-Food Credit (SCBs) 100.1 100.0 100.0 99.6 99.5 100.1 100.0 99.9 100.3 100.4 100.1 100.1 A.3.3 Investments (SCBs) 99.6 99.7 100.0 101.2 100.9 101.4 100.8 99.7 99.3 99.0 99.4 99.3 Price Indices [ CPI: 21 series and WPI: 8 series] B. CPI (Base: 2012 = 100) All Commodities 99.1 99.5 100.0 100.7 100.5 100.5 101.0 101.0 100.2 99.6 99.1 98.9 B.1 CPI - Food and beverages 98.2 98.7 100.0 100.9 101.0 101.0 102.2 102.0 100.7 99.2 98.3 97.9 B.1.1 CPI - Cereals and products 99.6 99.6 99.3 99.4 99.7 99.9 100.2 100.5 100.6 100.6 100.5 100.2 B.1 2 CPI - Meat and fish 99.8 101.9 104.1 102.0 99.4 100.0 100.2 99.0 98.0 98.7 98.3 98.5 B.1.3 CPI - Egg 94.7 95.7 99.3 100.2 97.2 97.9 99.7 102.5 105.5 106.7 102.6 98.5 B.1.4 CPI - Milk and products 99.9 100.1 100.1 100.0 100.1 100.1 100.1 100.0 100.0 99.9 99.9 99.9 B.1.5 CPI - Fruits 103.7 102.2 101.2 103.5 102.8 100.5 100.0 98.8 97.2 96.0 96.3 97.9 B.1.6 CPI - Vegetables 87.5 90.3 98.7 105.8 106.4 108.7 115.3 111.9 104.1 94.0 89.2 87.4 B.1.6.1 CPI - Potato 82.7 93.3 102.4 113.6 116.0 114.4 116.3 115.4 105.0 87.5 75.8 76.7 B.1.6.2 CPI - Onion 77.4 74.0 81.3 93.2 100.5 107.1 120.7 136.2 122.0 104.8 96.3 83.9 B.1.6.3 CPI - Tomato 71.3 77.2 104.8 140.5 119.8 115.9 122.5 122.2 100.5 81.9 73.3 69.1 B.1.7 CPI - Pulses and products 98.6 98.1 99.0 99.5 99.8 101.3 102.1 102.5 101.9 100.6 99.0 97.7 B.1.8 CPI - Spices 98.8 98.7 98.7 99.5 100.1 100.9 101.3 101.6 101.3 100.7 99.7 98.9 B.1.9 CPI - Non-alcoholic beverages 99.9 99.9 100.0 99.9 100.0 100.0 100.0 100.1 100.1 100.1 100.0 100.0 B.1.10 CPI - Prepared meals, snacks, sweets etc. 99.9 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.1 100.0 B.2 CPI - Clothing and footwear 100.0 99.9 100.0 100.0 100.0 99.9 100.0 100.1 100.1 100.1 100.0 100.0 B.3 CPI - Housing 100.5 100.3 99.5 99.7 100.1 99.8 100.5 100.4 99.5 99.8 100.1 99.8 B.4 CPI - Miscellaneous 100.0 100.0 100.0 100.2 100.1 100.1 100.1 100.0 99.9 99.9 99.9 99.9 C.1 Consumer Price Index for Industrial Workers 99.4 99.4 100.0 100.7 100.5 100.5 100.9 100.7 100.0 99.7 99.2 99.0 (Base: 2016=100) C.2 Consumer Price Index for Agricultural Labourers 99.2 99.3 99.6 99.9 100.2 100.3 100.8 101.0 100.7 100.2 99.6 99.2 (Base: 1986-87=100) C.3 Consumer Price Index for Rural Labourers 99.2 99.3 99.7 100.0 100.2 100.3 100.7 100.9 100.6 100.1 99.6 99.2 (Base: 1986-87=100) D. WPI (Base: 2011-12=100) All Commodities 100.1 99.8 100.1 100.5 100.1 100.0 100.7 100.7 99.8 99.5 99.4 99.4 D.1 WPI – Primary Articles 99.4 99.2 100.2 101.1 101.1 100.4 102.3 102.1 100.0 98.6 97.8 97.7 D.1.1 WPI - Food Articles 98.7 98.9 100.6 102.3 101.2 101.4 103.7 102.7 99.5 97.6 96.6 96.4 D.2 WPI – Fuel & Power 100.0 99.9 99.0 99.6 99.1 99.1 99.9 100.9 100.8 100.5 101.1 100.4 D.3 WPI – Manufactured Products 100.6 100.6 100.2 99.9 99.8 99.8 100.0 99.8 99.6 99.7 99.9 100.0 D.3.1 WPI - Manufacture of Food Products 100.5 100.1 100.1 99.7 100.2 100.1 100.0 100.6 100.0 99.7 99.5 99.7 D.3.2 WPI - Manufacture of Chemicals & Chemical 100.3 100.4 100.2 100.1 99.9 99.8 100.0 99.8 99.7 99.8 99.9 100.1 Products D.3.3 WPI - Manufacture of Basic Metals 101.3 101.9 100.8 99.6 99.3 99.3 99.9 99.2 99.0 99.3 99.8 100.3 RBI Bulletin November 2025 121ARTICLE Seasonality in Key Economic Indicators of India SERIES NAME APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR 1 2 3 4 5 6 7 8 9 10 11 12 13 Index of Industrial Production (23 series) E. IIP (Base 2011-12 = 100) General Index 97.7 100.9 99.6 98.3 96.4 96.8 99.4 97.6 103.4 104.0 98.1 107.6 E.1.1 IIP - Primary goods 99.5 104.7 101.3 98.2 95.6 93.1 97.2 96.0 102.0 104.2 98.1 109.9 E.1.2 IIP - Capital goods 88.6 96.9 102.4 98.1 96.8 104.1 99.8 95.4 98.8 101.9 99.7 117.6 E.1.3 IIP - Intermediate goods 98.0 100.9 98.5 101.0 99.7 98.1 99.6 97.3 102.4 103.1 96.0 105.3 E.1.4 IIP - Infrastructure/ construction goods 99.4 101.0 99.0 97.3 98.0 97.1 99.2 93.8 102.2 104.6 99.2 108.8 E.1.5 IIP - Consumer goods 96.0 98.5 96.6 99.2 97.3 99.7 98.0 101.7 106.6 104.2 97.9 103.7 E.1.5.1 IIP - Consumer durables 95.4 100.2 100.3 101.3 100.0 105.4 106.0 96.9 95.7 97.9 96.1 105.0 E.1.5.2 IIP - Consumer non-durables 97.8 97.3 95.1 97.7 94.5 94.8 97.1 105.3 113.2 107.8 98.0 101.0 E.2.1 IIP - Mining 98.4 102.1 96.6 88.4 85.4 84.8 96.8 100.9 107.5 111.3 106.2 121.4 E.2.2 IIP - Manufacturing 96.9 100.2 98.7 99.3 97.4 98.1 98.6 98.4 104.0 103.8 97.7 106.7 E.2.2.1 IIP - Manufacture of food products 96.6 89.7 87.3 89.9 91.2 88.3 93.2 106.8 119.3 119.7 111.3 107.0 E.2.2.2 IIP - Manufacture of beverages 107.7 118.1 110.6 98.7 89.6 90.1 88.8 87.9 90.8 100.3 102.3 115.5 E.2.2.3 IIP - Manufacture of textiles 98.4 99.0 97.9 99.8 99.5 99.7 102.1 98.8 104.0 102.3 96.4 102.0 E.2.2.4 IIP - Manufacture of chemicals and chemical 97.1 103.1 102.0 104.0 100.8 99.7 101.0 96.0 100.3 100.2 94.5 101.1 products E.2.2.5 IIP - Manufacture of motor vehicles, trailers and 96.2 100.9 98.1 101.9 98.3 99.9 103.5 99.8 92.1 104.7 100.4 104.1 semi-trailers E.2.3 IIP - Electricity 102.4 108.4 106.9 105.3 104.3 101.3 100.5 88.0 93.0 96.7 91.4 101.5 E.3 Cement Production 103.3 101.3 104.4 92.7 91.1 90.2 97.9 90.0 103.4 104.9 102.9 117.5 E.4 Steel Production 99.4 100.4 97.5 97.3 98.5 97.5 99.7 96.1 102.2 105.3 98.4 107.5 E.5 Coal Production 93.3 96.6 92.0 84.6 78.9 79.7 93.8 102.1 111.0 118.1 112.9 136.8 E.6 Crude Oil Production 99.3 102.7 99.0 101.8 100.9 97.3 101.1 98.3 101.9 102.5 92.4 102.8 E.7 Petroleum Refinery Production 99.3 102.5 98.2 100.1 96.1 93.5 96.5 99.4 105.6 105.4 96.3 106.8 E.8 Fertiliser Production 86.3 101.3 100.9 103.5 103.8 101.6 104.5 103.1 104.8 103.9 94.1 91.7 E.9 Natural Gas Production 96.2 100.6 98.4 102.3 102.2 99.6 103.3 99.5 102.2 102.4 91.9 101.6 Service Sector Indicators (5 series) F.1 Cargo handled at Major Ports 99.9 102.8 98.2 98.3 96.9 92.3 97.9 97.7 102.5 105.3 97.8 110.2 F.2 Railway Freight Traffic 98.4 103.4 99.1 96.6 95.0 92.3 97.0 96.9 103.8 105.5 97.6 114.6 F.3 Passenger flown (Km) - Domestic 100.4 104.7 98.2 94.7 95.9 93.6 99.0 101.5 107.7 103.1 98.6 102.4 F.4 Passenger flown (Km) - International 93.0 100.0 100.1 102.8 101.6 94.9 96.1 97.4 106.0 108.5 97.7 101.5 F.5 Passenger Vehicle Sales (wholesale) 94.4 95.6 93.5 99.3 100.1 104.0 113.5 97.5 86.5 108.5 103.3 103.4 Merchandise Trade (3 series) G.1 Exports 98.9 102.2 98.3 98.1 95.6 96.1 94.6 92.9 105.1 99.9 102.1 116.7 G.2 Imports 93.0 101.8 96.7 99.9 105.9 99.6 107.4 100.1 101.5 95.4 94.2 103.7 G.3 Non-Oil, Non-Gold and Non-Silver Imports 93.6 99.2 101.2 103.9 101.2 101.7 104.7 97.0 103.9 99.2 91.8 102.5 Payment System Indicators (4 series) H.1 RTGS 91.5 93.7 102.1 96.9 94.2 103.7 100.2 94.4 111.7 97.6 92.2 121.6 H.2 Paper Clearing 109.1 101.6 95.5 100.4 94.4 95.1 99.7 93.8 101.7 97.2 95.4 115.9 H.3 REC 93.8 96.7 97.3 99.3 96.0 98.0 102.9 94.2 103.2 99.2 94.2 125.4 H.4 Cards 98.2 101.5 97.3 102.9 100.5 97.9 111.2 96.7 102.6 100.1 89.8 102.2 122 RBI Bulletin November 2025Seasonality in Key Economic Indicators of India ARTICLE Annex - II Table A2-Q1: Time Period Used for Estimating Quarterly Seasonal Factors Quarterly Series Industrial Outlook Survey (12 series) National Accounts Statistics (8 series) L.1 Production Assessment I.1 Real Gross Domestic Product (GDP) L.2 Production Expectation I.2 Real Gross Value Added (GVA) L.3 Order Books Assessment I.3 Real PFCE L.4 Order Books Expectation I.4 Real GFCE Q1:2011-12 to L.5 Capacity Utilisation Assessment I.5 Real GFCF Q4:2024-2025 L.6 Capacity Utilisation Expectation Q1:2000-01 to I.6 GVA of Agriculture L.7 Selling Price Assessment Q4:2024-2025 I.7 GVA of Industry L.8 Selling Price Expectation I.8 GVA of Services L.9 Profit Margin Assessment Balance of Payments (4 series) L.10 Profit Margin Expectation J.1 Exports of Services L.11 Business Assessment Index J.2 Exports in Travel L.12 Business Expectation Index Q1:2011-12 to J.3 Exports in Telecommunications, Computer and Q4:2024-2025 Information Services J.4 Imports in Travel OBICUS (1 series) K.1 Capacity Utilisation of manufacturing companies Q1:2008-09 to Q4:2024-2025 RBI Bulletin November 2025 123ARTICLE Seasonality in Key Economic Indicators of India Table A2-Q2: Average* Quarterly Seasonal Factors of Select Economic Time Series (Per cent) SERIES NAME Q1 Q2 Q3 Q4 1 2 3 4 5 National Accounts Statistics (8 series) I.1 Real Gross Domestic Product (GDP) 98.1 97.8 99.5 104.7 I.2 Real Gross Value Added (GVA) 100.1 98.3 99.7 102.1 I.3 Real PFCE 97.6 96.0 104.4 102.0 I.4 Real GFCE 106.4 103.4 89.5 102.5 I.5 Real GFCF 100.2 96.9 97.4 105.5 I.6 GVA of Agriculture 91.8 77.3 125.1 105.9 I.7 GVA of Industry 99.7 98.0 95.3 107.3 I.8 GVA of Services 102.1 103.5 95.2 99.3 Balance of Payments (4 series) J.1 Exports of Services 96.5 98.5 102.4 102.6 J.2 Exports in Travel 85.9 95.2 109.1 110.2 J.3 Exports in Telecommunications, Computer and Information Services 99.0 100.7 101.3 99.1 J.4 Imports in Travel 108.2 107.2 92.3 92.4 OBICUS (1 series) K.1 Capacity Utilisation of manufacturing companies 98.3 99.5 100.1 102.0 Industrial Outlook Survey (12 series) L.1 Production Assessment 99.2 97.9 99.9 103.0 L.2 Production Expectation 98.9 100.7 100.8 99.5 L.3 Order Books Assessment 100.5 98.4 99.0 102.1 L.4 Order Books Expectation 99.3 100.2 100.1 100.4 L.5 Capacity Utilisation Assessment 99.4 98.4 99.0 103.3 L.6 Capacity Utilisation Expectation 99.5 99.2 100.9 100.2 L.7 Selling Price Assessment 102.8 97.9 98.5 100.8 L.8 Selling Price Expectation 100.4 100.1 100.0 99.5 L.9 Profit Margin Assessment 100.3 99.5 98.7 101.5 L.10 Profit Margin Expectation 100.5 100.5 99.7 99.3 L.11 Business Assessment Index 100.0 99.2 99.6 101.2 L.12 Business Expectation Index 99.2 99.3 101.0 100.4 Note: *: Average of last ten years’ quarterly seasonal factors, in general. Here, the average quarterly seasonal factors have been computed on the basis of last 10 years (i.e., Q1: 2015-16 to Q4: 2024-25). Numbers marked in ‘bold’ are peaks and troughs of respective series. 124 RBI Bulletin November 2025Seasonality in Key Economic Indicators of India ARTICLE Table A2-Q3: Range (Difference Between Peak and Trough) of Quarterly Seasonal Factors (Percentage points) Average SERIES \ YEAR 2015-16 2016-17 2017-18 2018-19 2019-20 2020-21 2021-22 2022-23 2023-24 2024-25 Range 1 2 3 4 5 6 7 8 9 10 11 12 National Accounts Statistics (8 series) I.1 Real Gross Domestic Product (GDP) 5.1 5.1 5.5 6.0 6.6 7.0 7.6 8.2 8.7 9.1 6.9 I.2 Real Gross Value Added (GVA) 1.7 1.7 2.0 2.6 3.2 4.1 4.9 5.6 6.1 6.4 3.8 I.3 Real PFCE 7.2 6.8 6.8 7.4 8.0 8.7 9.4 9.9 10.1 10.0 8.4 I.4 Real GFCE 30.8 29.6 25.4 18.2 19.1 20.8 27.0 32.5 33.6 32.6 16.9 I.5 Real GFCF 6.5 6.8 7.6 8.7 9.6 10.1 10.2 10.8 11.4 11.5 8.6 I.6 GVA of Agriculture 52.2 50.3 48.8 48.0 47.3 46.8 46.2 46.0 46.1 46.4 47.8 I.7 GVA of Industry 10.0 10.3 10.5 10.6 11.1 11.8 12.7 13.7 14.4 14.8 12.0 I.8 GVA of Services 10.4 10.2 9.8 9.4 8.9 8.3 7.5 6.8 6.2 5.9 8.3 Balance of Payments (4 series) J.1 Exports of Services 5.5 5.6 5.8 6.1 6.5 6.7 6.7 6.6 6.6 6.6 6.1 J.2 Exports in Travel 20.9 18.7 17.5 18.5 21.7 26.4 30.1 31.2 33.7 34.8 24.3 J.3 Exports in Telecommunications, 3.0 2.9 2.5 2.2 2.0 2.0 2.0 2.4 2.8 3.1 2.4 Computer and Information Services J.4 Imports in Travel 18.4 18.9 18.1 17.6 16.4 15.7 15.1 14.9 14.9 15.2 15.9 OBICUS (1 series) K.1 Capacity Utilisation of 4.3 3.8 3.3 3.1 3.1 3.3 3.7 3.9 4.0 4.1 3.7 manufacturing companies Industrial Outlook Survey (12 series) L.1 Production Assessment 4.6 4.6 4.8 4.9 4.9 5.0 5.3 5.3 5.5 5.6 5.0 L.2 Production Expectation 3.3 2.8 2.5 2.1 2.0 2.6 2.8 2.5 1.9 1.8 2.0 L.3 Order Books Assessment 4.2 4.0 3.8 3.7 3.6 3.5 3.6 3.8 3.8 3.9 3.7 L.4 Order Books Expectation 1.5 0.9 0.9 1.0 0.9 0.8 1.2 1.9 2.3 2.4 1.1 L.5 Capacity Utilisation Assessment 4.6 4.5 4.4 4.5 4.6 4.8 5.0 5.3 5.5 5.7 4.9 L.6 Capacity Utilisation Expectation 3.0 2.1 1.5 1.4 1.6 1.9 2.1 2.3 2.7 3.0 1.7 L.7 Selling Price Assessment 3.6 4.4 5.2 5.7 5.9 5.9 5.7 5.0 4.4 4.0 5.0 L.8 Selling Price Expectation 1.3 1.4 1.6 1.9 2.0 1.6 1.4 1.3 1.3 1.4 0.9 L.9 Profit Margin Assessment 3.6 3.5 3.4 3.2 2.9 2.7 2.7 2.5 2.2 1.6 2.8 L.10 Profit Margin Expectation 1.3 1.2 1.4 1.9 2.4 2.6 2.3 1.7 1.0 0.7 1.2 L.11 Business Assessment Index 2.5 2.4 2.3 2.2 2.0 1.7 1.6 1.5 1.6 1.8 2.0 L.12 Business Expectation Index 1.5 1.5 1.6 1.7 1.8 1.8 1.9 2.0 2.2 2.3 1.8 Note: Average seasonal factor range is the range of average seasonal factors for last ten years; range is calculated as the difference between maximum and minimum of quarterly seasonal factors. RBI Bulletin November 2025 125ARTICLE Seasonality in Key Economic Indicators of India Table A2-Q4: Major Diagnostics of all the Quarterly Indicators Seasonality in Original Residual Seasonality Quality Diagnostics Series Name of variable F test KW test F test F test 3 yr M7 Q p-value p-value p-value p-value I.1 Real Gross Domestic Product (GDP) 0.00 0.00 0.06 0.42 0.23 0.27 I.2 Real Gross Value Added (GVA) 0.00 0.00 0.02 0.10 0.46 0.46 I.3 Real PFCE 0.00 0.00 0.04 0.45 0.19 0.52 I.4 Real GFCE 0.00 0.00 0.33 0.62 0.71 1.00 I.5 Real GFCF 0.00 0.00 0.15 0.36 0.32 0.67 I.6 GVA of Agriculture 0.00 0.00 0.74 0.66 0.07 0.13 I.7 GVA of Industry 0.00 0.00 0.16 0.80 0.23 0.38 I.8 GVA of Services 0.00 0.00 0.02 0.14 0.27 0.32 J.1 Exports of Services 0.00 0.00 0.43 0.52 0.30 0.36 J.2 Exports in Travel 0.00 0.00 0.94 0.54 0.42 0.47 J.3 Exports in Telecommunications, Computer 0.00 0.00 0.09 0.65 0.49 0.48 and Information Services J.4 Imports of Services 0.04 0.01 0.91 0.86 1.19 1.00 J.5 Imports in Travel 0.00 0.00 0.44 0.98 0.32 0.29 J.6 Imports in Telecommunications, Computer 0.02 0.04 0.82 0.63 1.20 1.47 and Information Services K.1 Capacity Utilisation of manufacturing 0.00 0.00 0.18 0.54 0.29 0.55 companies L.1 Production Assessment 0.00 0.00 0.16 0.85 0.31 0.45 L.2 Production Expectation 0.00 0.00 0.77 0.64 0.57 0.63 L.3 Order Books Assessment 0.00 0.00 0.13 0.69 0.56 0.71 L.4 Order Books Expectation 0.00 0.00 0.36 0.95 0.82 0.95 L.5 Employment Assessment 0.24 0.02 0.30 0.98 2.03 1.07 L.6 Employment Expectation 0.09 0.03 0.64 0.69 1.57 1.09 L.7 Capacity Utilisation Assessment 0.00 0.00 0.08 0.94 0.25 0.44 L.8 Capacity Utilisation Expectation 0.00 0.00 0.92 0.93 0.74 0.98 L.9 Selling Price Assessment 0.00 0.00 0.40 0.74 0.49 0.82 L.10 Selling Price Expectation 0.03 0.00 0.52 0.70 1.27 1.22 L.11 Cost of External Finance Assessment 0.64 0.57 0.27 0.99 2.71 1.32 L.12 Cost of External Finance Expectation 0.02 0.01 0.33 0.96 1.41 0.84 L.13 Profit Margin Assessment 0.00 0.00 0.16 0.95 0.76 1.05 L.14 Profit Margin Expectation 0.31 0.12 0.70 0.45 2.13 1.19 L.15 Business Assessment Index 0.00 0.00 0.30 0.94 0.51 0.75 L.16 Business Expectation Index 0.00 0.00 0.97 0.90 0.56 0.86 Note: Please see notes to Table A1-M4. 126 RBI Bulletin November 2025Seasonality in Key Economic Indicators of India ARTICLE Table A2-Q5: Quarterly Seasonal Factors of Select Economic Time Series for 2024-25 (Per cent) SERIES NAME Q1 Q2 Q3 Q4 1 2 3 4 5 National Accounts Statistics (8 series) I.1 Real Gross Domestic Product (GDP) 97.3 97.0 99.7 106.1 I.2 Real Gross Value Added (GVA) 99.2 97.4 99.7 103.8 I.3 Real PFCE 97.2 95.8 105.8 101.1 I.4 Real GFCE 100.6 93.1 87.0 119.5 I.5 Real GFCF 99.6 98.4 95.2 106.6 I.6 GVA of Agriculture 91.1 77.5 123.9 107.6 I.7 GVA of Industry 98.8 97.6 94.4 109.2 I.8 GVA of Services 101.1 102.0 96.0 101.1 Balance of Payments (4 series) J.1 Exports of Services 96.2 98.9 102.8 102.0 J.2 Exports in Travel 82.7 88.8 117.4 110.7 J.3 Exports in Telecommunications, Computer and Information Services 98.7 100.0 101.7 99.6 J.4 Imports in Travel 107.8 106.0 92.6 93.4 OBICUS (1 series) K.1 Capacity Utilisation of manufacturing companies 98.5 99.1 99.9 102.5 Industrial Outlook Survey (12 series) L.1 Production Assessment 99.2 97.6 100.0 103.2 L.2 Production Expectation 99.0 100.8 100.7 99.7 L.3 Order Books Assessment 100.4 98.1 99.5 102.1 L.4 Order Books Expectation 98.4 100.7 100.8 100.1 L.5 Capacity Utilisation Assessment 98.8 97.9 99.8 103.6 L.6 Capacity Utilisation Expectation 100.0 98.5 101.5 100.0 L.7 Selling Price Assessment 102.4 98.3 99.1 100.2 L.8 Selling Price Expectation 99.2 100.6 100.4 100.0 L.9 Profit Margin Assessment 99.4 100.6 99.2 100.8 L.10 Profit Margin Expectation 100.0 100.1 99.8 100.4 L.11 Business Assessment Index 99.7 99.5 99.6 101.2 L.12 Business Expectation Index 99.0 99.4 101.3 100.4 RBI Bulletin November 2025 127CURRENT STATISTICS Select Economic Indicators Reserve Bank of India Money and Banking Prices and Production Government Accounts and Treasury Bills Financial Markets External Sector Payment and Settlement Systems Occasional SeriesCURRENT STATISTICS Contents No. Title Page 1 Select Economic Indicators 131 Reserve Bank of India 2 RBI – Liabilities and Assets 132 3 Liquidity Operations by RBI 133 4 Sale/ Purchase of U.S. Dollar by the RBI 134 4A Maturity Breakdown (by Residual Maturity) of Outstanding Forwards of RBI (US$ Million) 135 5 RBI's Standing Facilities 135 Money and Banking 6 Money Stock Measures 136 7 Sources of Money Stock (M) 137 3 8 Monetary Survey 138 9 Liquidity Aggregates 139 10 Reserve Bank of India Survey 140 11 Reserve Money – Components and Sources 140 12 Commercial Bank Survey 141 13 Scheduled Commercial Banks' Investments 141 14 Business in India – All Scheduled Banks and All Scheduled Commercial Banks 142 15 Deployment of Gross Bank Credit by Major Sectors 143 16 Industry-wise Deployment of Gross Bank Credit 144 17 State Co-operative Banks Maintaining Accounts with the Reserve Bank of India 145 18 (a) Flow of Financial Resources to Commercial Sector in India 146 18 (b) Outstanding Credit to Commercial Sector in India 147 Prices and Production 19 Consumer Price Index (Base: 2012=100) 148 20 Other Consumer Price Indices 148 21 Monthly Average Price of Gold and Silver in Mumbai 148 22 Wholesale Price Index 149 23 Index of Industrial Production (Base: 2011-12=100) 153 Government Accounts and Treasury Bills 24 Union Government Accounts at a Glance 153 25 Treasury Bills – Ownership Pattern 154 26 Auctions of Treasury Bills 154 Financial Markets 27 Daily Call Money Rates 155 28 Certificates of Deposit 156 29 Commercial Paper 156 RBI Bulletin November 2025 129CURRENT STATISTICS No. Title Page 30 Average Daily Turnover in Select Financial Markets 156 31 New Capital Issues by Non-Government Public Limited Companies 157 External Sector 32 Foreign Trade 158 33 Foreign Exchange Reserves 158 34 Non-Resident Deposits 158 35 Foreign Investment Inflows 159 36 Outward Remittances under the Liberalised Remittance Scheme (LRS) for Resident Individuals 159 37 Indices of Nominal Effective Exchange Rate (NEER) and Real Effective Exchange Rate (REER) of the Indian Rupee 160 38 External Commercial Borrowings (ECBs) – Registrations 161 39 India’s Overall Balance of Payments (US $ Million) 162 40 India's Overall Balance of Payments (` Crore) 163 41 Standard Presentation of BoP in India as per BPM6 (US $ Million) 164 42 Standard Presentation of BoP in India as per BPM6 (` Crore) 165 43 India’s International Investment Position 166 Payment and Settlement Systems 44 Payment System Indicators 167 Occasional Series 45 Small Savings 169 46 Ownership Pattern of Central and State Governments Securities 170 47 Combined Receipts and Disbursements of the Central and State Governments 171 48 Financial Accommodation Availed by State Governments under various Facilities 172 49 Investments by State Governments 173 50 Market Borrowings of State Governments 174 51 (a) Flow of Financial Assets and Liabilities of Households - Instrument-wise 175 51 (b) Stocks of Financial Assets and Liabilities of Households- Select Indicators 178 Notes: .. = Not available. – = Nil/Negligible. P = Preliminary/Provisional. PR = Partially Revised. 130 RBI Bulletin November 2025CURRENT STATISTICS No. 1: Select Economic Indicators 2023-24 2024-25 2025-26 Item 2024-25 Q4 Q1 Q4 Q1 1 2 3 4 5 1 Real Sector (% Change) 1.1 GVA at Basic Prices 6.4 7.3 6.5 6.8 7.6 1.1.1 Agriculture 4.6 0.9 1.5 5.4 3.7 1.1.2 Industry 4.5 9.9 7.8 4.7 5.8 1.1.3 Services 7.5 8.0 7.2 7.9 9.0 1.1a Final Consumption Expenditure 6.5 6.3 7.0 4.7 7.1 1.1b Gross Fixed Capital Formation 7.1 6.0 6.7 9.4 7.8 2024 2025 2024-25 Aug. Sep. Aug. Sep. 1 2 3 4 5 1.2 Index of Industrial Production 4. 0 0. 0 3 . 2 4 . 1 4 . 0 2 Money and Banking (% Change) 2.1 Scheduled Commercial Banks 2.1.1 Deposits 10.3 11.9 10.4 9.3 9.4 2.1.2 Credit # 11.0 13.1 12.3 10.1 10.8 2.1.2.1 Non-food Credit # 11.0 13.1 12.4 10.0 10.7 2.1.3 Investment in Govt. Securities 9. 7 6. 3 6 . 8 6 . 7 6 . 5 2.2 Money Stock Measures 2.2.1 Reserve Money (M0) 4.3 4.8 6.0 5.8 4.5 2.2.2 Broad Money (M3) 9.4 9.8 10.4 9.8 9.2 3 Ratios (%) 3.1 Cash Reserve Ratio 4.00 4.50 4.50 4.00 3.75 3.2 Statutory Liquidity Ratio 18.00 18.00 18.00 18.00 18.00 3.3 Cash-Deposit Ratio 4.3 5.1 5.1 4.4 4.1 3.4 Credit-Deposit Ratio 80.8 78.4 79.2 79.0 80.2 3.5 Incremental Credit-Deposit Ratio # 86.1 47.7 61.6 43.3 69.0 3.6 Investment-Deposit Ratio 29.7 29.3 29.6 28.6 28.8 3.7 Incremental Investment-Deposit Ratio 28.1 20.8 26.2 7.7 13.2 4 Interest Rates (%) 4.1 Policy Repo Rate 6.25 6.50 6.50 5.50 5.50 4.2 Fixed Reverse Repo Rate 3.35 3.35 3.35 3.35 3.35 4.3 Standing Deposit Facility (SDF) Rate * 6.00 6.25 6.25 5.25 5.25 4.4 Marginal Standing Facility (MSF) Rate 6.50 6.75 6.75 5.75 5.75 4.5 Bank Rate 6.50 6.75 6.75 5.75 5.75 4.6 Base Rate 9.10/10.40 9.10/10.40 9.10/10.40 8.50/10.30 8.50/10.30 4.7 MCLR (Overnight) 8.15/8.45 8.15/8.45 8.15/8.45 7.80/8.15 7.80/8.00 4.8 Term Deposit Rate >1 Year 6.00/7.25 6.00/7.25 6.00/7.25 5.85/6.60 5.85/6.60 4.9 Savings Deposit Rate 2.70/3.00 2.70/3.00 2.70/3.00 2.50/2.50 2.50/2.50 4.10 Call Money Rate (Weighted Average) 6.35 6.59 6.61 5.45 5.57 4.11 91-Day Treasury Bill (Primary) Yield 6.52 6.63 6.65 5.51 5.47 4.12 182-Day Treasury Bill (Primary) Yield 6.52 6.72 6.72 5.60 5.58 4.13 364-Day Treasury Bill (Primary) Yield 6.47 6.72 6.70 5.64 5.61 4.14 10-Year G-Sec Par Yield (FBIL) 6.62 6.90 6.78 6.67 6.61 5 Reference Rate and Forward Premia 5.1 INR-US$ Spot Rate (Rs. Per Foreign Currency) 85.5 8 83.8 7 83.6 7 87.8 5 88.7 2 5.2 INR-Euro Spot Rate (Rs. Per Foreign Currency) 92.32 92.91 93.46 102.47 103.62 5.3 Forward Premia of US$ 1-month (%) 3.12 1.12 1.65 1.76 2.19 3-month (%) 2.56 1.34 1.74 1.80 2.17 6-month (%) 2.28 1.64 2.11 1.97 2.23 6 Inflation (%) 6.1 All India Consumer Price Index 4.6 3.7 5.5 2.1 1.4 6.2 Consumer Price Index for Industrial Workers 3.39 2.4 4.2 3.2 2.8 6.3 Wholesale Price Index 2.3 1.2 1.9 0.5 0.1 6.3.1 Primary Articles 5.2 2.5 6.5 -2.1 -3.3 6.3.2 Fuel and Power -1.3 -0.5 -3.9 -3.2 -2.6 6.3.3 Manufactured Products 1.7 1.0 1.1 2.6 2.3 7 Foreign Trade (% Change) 7.1 Imports 6.9 10.4 8.3 -10.1 16.7 7.2 Exports 0. 1 -14. 1 -1 . 0 6 . 1 6 . 7 Note : Financial Benchmark India Pvt. Ltd. (FBIL) has commenced publication of the G-Sec benchmarks with effect from March 31, 2018 as per RBI circularFMRD.DIRD. 7/14.03.025/2017-18 dated March 31, 2018. FBIL has started dissemination of reference rates w.e.f. July 10, 2018. #: Bank credit growth and related ratios for all fortnights from December 3, 2021 to November 18, 2022 are adjusted for past reporting errors by select scheduled commercial banks (SCBs). Data include the impact of merger of a non-bank with a bank w.e.f. July 1, 2023. *: As per Press Release No. 2022-2023/41 dated April 08, 2022. RBI Bulletin November 2025 131CURRENT STATISTICS Reserve Bank of India No. 2: RBI - Liabilities and Assets * (₹ Crore) Item As on the Last Friday/ Friday 2024-25 2024 2025 Oct. Oct. 03 Oct. 10 Oct. 17 Oct. 24 Oct. 31 1 2 3 4 5 6 7 1 Issue Department 1.1 Liabilities 1.1.1 Notes in Circulation 3683836 3499601 3761894 3770178 3790325 3795985 3780994 1.1.2 Notes held in Banking Department 11 15 13 12 15 12 12 1.1/1.2 Total Liabilities (Total Notes Issued) or Assets 3683847 3499616 3761908 3770190 3790340 3795998 3781006 1.2 Assets 1.2.1 Gold 235379 205953 310816 321803 338459 328604 320090 1.2.2 Foreign Securities 3448129 3293260 3450711 3448113 3451327 3466899 3460480 1.2.3 Rupee Coin 340 402 381 274 553 496 436 1.2.4 Government of India Rupee Securities - - - - - - - 2 Banking Department 2.1 Liabilities 2.1.1 Deposits 1709285 1727119 1629157 1654275 1568937 1558226 1542156 2.1.1.1 Central Government 100 101 100 100 100 100 100 2.1.1.2 Market Stabilisation Scheme - 2.1.1.3 State Governments 42 42 43 42 42 42 42 2.1.1.4 Scheduled Commercial Banks 943060 1042981 883596 833283 827461 823260 842947 2.1.1.5 Scheduled State Co-operative Banks 7776 8265 7640 7009 7123 7055 7077 2.1.1.6 Non-Scheduled State Co-operative Banks 5963 5434 5070 4883 5071 4769 4612 2.1.1.7 Other Banks 46963 50532 45442 42732 42761 43086 43820 2.1.1.8 Others 593085 465711 539838 603316 515704 521552 481530 2.1.1.9 Financial Institution Outside India 112296 154053 147428 162911 170674 158362 162026 2.1.2 Other Liabilities 2150508 1925821 2582289 2572197 2622955 2565552 2574393 2.1/2.2 Total Liabilities or Assets 3859793 3652940 4211446 4226472 4191892 4123778 4116549 2.2 Assets 2.2.1 Notes and Coins 11 15 13 12 15 12 12 2.2.2 Balances Held Abroad 1413591 1736918 1708880 1655790 1596215 1539478 1581443 2.2.3 Loans and Advances 2.2.3.1 Central Government - - - - - - - 2.2.3.2 State Governments 26284 24079 22137 16116 30095 23777 20016 2.2.3.3 Scheduled Commercial Banks 251984 30948 1512 40552 8733 35276 5489 2.2.3.4 Scheduled State Co-op.Banks - - - - - - - 2.2.3.5 Industrial Dev. Bank of India - - - - - - - 2.2.3.6 NABARD - - - - - - - 2.2.3.7 EXIM Bank - - - - - - - 2.2.3.8 Others 36426 8699 10550 10395 12879 13579 11539 2.2.3.9 Financial Institution Outside India 111768 153006 146618 162322 170809 158848 162524 2.2.4 Bills Purchased and Discounted 2.2.4.1 Internal - - - - - - - 2.2.4.2 Government Treasury Bills - - - - - - - 2.2.5 Investments 1560630 1313046 1737057 1736207 1737400 1734748 1732352 2.2.6 Other Assets 459101 386229 584678 605078 635746 618060 603174 2.2.6.1 Gold 429510 370227 566080 586091 616427 598477 582971 * Data are provisional. 132 RBI Bulletin November 2025CURRENT STATISTICS No. 3: Liquidity Operations by RBI (₹ Crore) Date Standing OMO (Outright) Net Injection (+)/ Liquidity Absorption (-) Liquidity Adjustment Facility Facilities (1+3+5+7+9-2-4-6 -8) Sale Purchase Variable Variable Reverse Rate Repo Rate MSF SDF Repo Reverse Repo Repo 1 2 3 4 5 6 7 8 9 10 Sep. 1, 2025 - - - 48820 4571 118298 - - - -162547 Sep. 2, 2025 - - - - 1510 127443 -1214 - - -127147 Sep. 3, 2025 - - - - 1952 118742 -3131 - - -119921 Sep. 4, 2025 - - - 150023 4644 149334 1149 - - -293564 Sep. 5, 2025 - - - - 3936 149276 - - - -145340 Sep. 6, 2025 - - - - 868 109040 - - - -108172 Sep. 7, 2025 - - - - 824 104586 - - - -103762 Sep. 8, 2025 - - - - 12069 104910 - - - -92841 Sep. 9, 2025 - - - - 1052 123828 - - - -122776 Sep. 10, 2025 - - - 20175 967 99886 -577 - - -119671 Sep. 11, 2025 - - - - 1159 124040 576 - - -122305 Sep. 12, 2025 - - - 150015 1184 123250 - - - -272081 Sep. 13, 2025 - - - - 969 112438 - - - -111469 Sep. 14, 2025 - - - - 1176 115495 - - - -114319 Sep. 15, 2025 - - - - 1282 199673 718 - - -197673 Sep. 16, 2025 - - 585 - 18172 98460 649 - - -79054 Sep. 17, 2025 - - - - 1507 74961 - - - -73454 Sep. 18, 2025 - - 25006 - 2849 111533 1195 - - -82483 Sep. 19, 2025 - - 60357 - 310 124189 - - - -63522 Sep. 20, 2025 - - - - 438 77708 - - - -77270 Sep. 21, 2025 - - - - 230 73485 - - - -73255 Sep. 22, 2025 - - 21151 - 6679 66129 - - - -38299 Sep. 23, 2025 - - 140516 - 328 123947 - - - 16897 Sep. 24, 2025 - - 48980 - 403 93350 - - 10 -43957 Sep. 25, 2025 - - 69060 - 2688 95049 - - - -23301 Sep. 26, 2025 - - 90931 - 385 147590 165 - - -56109 Sep. 27, 2025 - - - - 2966 136939 - - - -133973 Sep. 28, 2025 - - - - 6922 137488 - - - -130566 Sep. 29, 2025 - - - - 1897 158209 -109 - - -156421 Sep. 30, 2025 - - 85197 - 1850 175443 - - - -88396 RBI Bulletin November 2025 133CURRENT STATISTICS No. 4: Sale/ Purchase of U.S. Dollar by the RBI i) Operations in onshore / offshore OTC segment Item 2024 2025 2024-25 Sep. Aug. Sep. 1 2 3 4 1 Net Purchase/ Sale of Foreign Currency (US $ Million) (1.1-1.2) -34511 9639 -7695 -7910 1.1 Purchase (+) 364200 28930 0 2200 1.2 Sale (–) 398711 19291 7695 10110 2 ₹ equivalent at contract rate (₹ Crores) -291233 80549 -67456 -69884 3 Cumulative (over end-March) (US $ Million) -34511 8547 -13792 -21702 (₹ Crore) -291233 70945 -121604 -191488 4 Outstanding Net Forward Sales (-)/ Purchase (+) at the end of month (US -84345 -14580 -53355 -59405 $ Million) ii) Operations in currency futures segment Item 2024 2025 2024-25 Sep. Aug. Sep. 1 2 3 4 1 Net Purchase/ Sale of Foreign Currency (US $ Million) (1.1-1.2) 0 0 0 0 1.1 Purchase (+) 31415 2149 0 1311 1.2 Sale (–) 31415 2149 0 1311 2 Outstanding Net Currency Futures Sales (-)/ Purchase (+) at the end of 0 -200 -450 -1605 month (US $ Million) 134 RBI Bulletin November 2025CURRENT STATISTICS No. 4 A : Maturity Breakdown (by Residual Maturity) of Outstanding Forwards of RBI (US $ Million) Item As on September 30 , 2025 Long (+) Short (-) Net (1-2) 1 2 3 1. Upto 1 month 0 16485 -16485 2. More than 1 month and upto 3 months 0 14830 -14830 3. More than 3 months and upto 1 year 0 7990 -7990 4. More than 1 year 0 20100 -20100 Total (1+2+3+4) 0 59405 -59405 No. 5: RBI’s Standing Facilities (₹ Crore) Item As on the Last Reporting Fortnights 2024-25 2024 2025 Oct. 18 May. 30 Jun. 27 Jul. 25 Aug. 22 Sep. 19 Oct. 31 1 2 3 4 5 6 7 8 1 MSF 9961 4216 1540 1065 1906 1818 310 5489 2 Export Credit Refinance for Scheduled Banks 2.1 Limit - - - - - - - - 2.2 Outstanding - - - - - - - - 3 Liquidity Facility for PDs 3.1 Limit 9900 9900 14900 14900 14900 14900 14900 14900 3.2 Outstanding 9517 7223 8595 7010 10299 10985 10319 11518 4 Others 4.1 Limit 76000 76000 76000 76000 76000 76000 76000 76000 4.2 Outstanding - - - - - - - - 5 Total Outstanding (1+2.2+3.2+4.2) 19478 11439 10135 8075 12205 12803 10629 17007 RBI Bulletin November 2025 135CURRENT STATISTICS Money and Banking No. 6: Money Stock Measures (₹ Crore) Item Outstanding as on March 31/last reporting Fortnights of the month/ reporting Fortnights 2024-25 2024 2025 Sep. 20 Aug. 22 Sep. 05 Sep. 19 1 2 3 4 5 1 Currency with the Public (1.1 + 1.2 + 1.3 – 1.4) 3630751 3392284 3715737 3718989 3706688 1.1 Notes in Circulation 3687816 3455943 3773434 3776964 3761714 1.2 Circulation of Rupee Coin 35889 33851 37314 37314 37695 1.3 Circulation of Small Coins 743 743 743 743 743 1.4 Cash on Hand with Banks 93696 98253 95755 96032 93464 2 Deposit Money of the Public 2953329 2735069 3142161 3145885 3121404 2.1 Demand Deposits with Banks 2840023 2640984 3032516 3035554 3004901 2.2 'Other' Deposits with Reserve Bank 113307 94085 109645 110330 116503 3 M1 (1 + 2) 6584081 6127353 6857897 6864874 6828092 4 Post Office Saving Bank Deposits 212331 200889 212331 212331 212331 5 M2 (3 + 4) 6796412 6328242 7070228 7077205 7040423 6 Time Deposits with Banks 20702508 19826970 21450604 21611139 21522189 7 M3 (3 + 6) 27286589 25954324 28308502 28476013 28350281 8 Total Post Office Deposits 1443555 1379283 1443555 1443555 1443555 9 M4 (7 + 8) 28730144 27333607 29752057 29919568 29793836 136 RBI Bulletin November 2025CURRENT STATISTICS No. 7 : Sources of Money Stock (M) 3 (₹ Crore) Sources Outstanding as on March 31/last reporting Fortnights of the month/reporting Fortnights 2024-25 2024 2025 Sep. 20 Aug. 22 Sep. 05 Sep. 19 1 2 3 4 5 1 Net Bank Credit to Government 8510825 7626155 8585943 8747071 8490309 1.1 RBI’s net credit to Government (1.1.1–1.1.2) 1508105 921112 1511706 1607508 1324495 1.1.1 Claims on Government 1591591 1340430 1795840 1815543 1805435 1.1.1.1 Central Government 1558903 1313984 1768377 1771473 1772990 1.1.1.2 State Governments 32688 26447 27463 44070 32445 1.1.2 Government deposits with RBI 83485 419318 284134 208035 480940 1.1.2.1 Central Government 83443 419275 284091 207993 480898 1.1.2.2 State Governments 42 42 43 42 42 1.2 Other Banks’ Credit to Government 7002720 6705043 7074237 7139564 7165814 2 Bank Credit to Commercial Sector 19068129 17889364 19449509 19600218 19709575 2.1 RBI’s credit to commercial sector 38246 10589 13069 9833 15669 2.2 Other banks’ credit to commercial sector 19029883 17878775 19436439 19590385 19693906 2.2.1 Bank credit by commercial banks 18243972 17125052 18646842 18800686 18902861 2.2.2 Bank credit by co-operative banks 766659 734471 769722 769542 770470 2.2.3 Investments by commercial and co-operative banks in other securities 19252 19252 19876 20158 20575 3 Net Foreign Exchange Assets of Banking Sector (3.1 + 3.2) 6148527 5991648 6467254 6581276 6597919 3.1 RBIs net foreign exchange assets (3.1.1 - 3.1.2) 5550947 5629250 5870992 5985014 6010871 3.1.1 Gross foreign assets 5550956 5629252 5870987 5985005 6010860 3.1.2 Foreign liabilities 9 2 -5 -9 -11 3.2 Other banks’ net foreign exchange assets 597580 362398 596262 596262 587048 4 Government’s Currency Liabilities to the Public 36632 34594 38057 38057 38438 5 Banking Sector’s Net Non-monetary Liabilities 6477524 5587438 6232262 6490610 6485961 5.1 Net non-monetary liabilities of RBI 2147427 1957330 2288911 2431271 2463314 5.2 Net non-monetary liabilities of other banks (residual) 4330098 3630108 3943351 4059339 4022647 M₃(1+2+3+4–5) 27286589 25954324 28308502 28476013 28350281 RBI Bulletin November 2025 137CURRENT STATISTICS No. 8: Monetary Survey (₹ Crore) Item Outstanding as on March 31/last reporting Fortnights of the month/reporting Fortnights 2024-25 2024 2025 Sep. 20 Aug. 22 Sep. 05 Sep. 19 1 2 3 4 5 Monetary Aggregates NM₁ (1.1+1.2.1+1.3) 6584081 6127353 6857897 6865254 6828092 NM₂ (NM₁ + 1.2.2.1) 15768688 14929483 16376038 16454216 16377198 NM₃ (NM₂ +1.2.2.2 + 1.4 = 2.1 + 2.2 + 2.3 – 2.4 – 2.5) 27909568 26567267 28871731 28990627 28906851 1 Components 1.1 Currency with the Public 3630751 3392284 3715737 3719370 3706688 1.2 Aggregate Deposits of Residents 23250261 22201272 24183940 24344358 24225137 1.2.1 Demand Deposits 2840023 2640984 3032516 3035554 3004901 1.2.2 Time Deposits of Residents 20410239 19560288 21151424 21308804 21220236 1.2.2.1 Short-term Time Deposits 9184607 8802130 9518141 9588962 9549106 1.2.2.1.1 Certificates of Deposits (CDs) 527375 467853 494788 495811 495098 1.2.2.2 Long-term Time Deposits 11225631 10758158 11633283 11719842 11671130 1.3 'Other' Deposits with RBI 113307 94085 109645 110330 116503 1.4 Call/Term Funding from Financial Institutions 915248 879626 862409 816569 858523 2 Sources 2.1 Domestic Credit 28802443 26693009 29305221 29627521 29479531 2.1.1 Net Bank Credit to the Government 8510825 7626155 8585943 8747071 8490309 2.1.1.1 Net RBI credit to the Government 1508105 921112 1511706 1607508 1324495 2.1.1.2 Credit to the Government by the Banking System 7002720 6705043 7074237 7139564 7165814 2.1.2 Bank Credit to the Commercial Sector 20291618 19066853 20719277 20880449 20989222 2.1.2.1 RBI Credit to the Commercial Sector 38246 10589 13069 9833 15669 2.1.2.2 Credit to the Commercial Sector by the Banking System 20253372 19056265 20706208 20870616 20973552 2.1.2.2.1 Other Investments ( Non-SLR Securities) 1208294 1162164 1253713 1268387 1264028 2.2 Government's Currency Liabilities to the Public 36632 34594 38057 38438 38438 2.3 Net Foreign Exchange Assets of the Banking Sector 5605462 5546567 5986001 6074543 6102612 2.3.1 Net Foreign Exchange Assets of the RBI 5550947 5629250 5870992 5985014 6010871 2.3.2 Net Foreign Currency Assets of the Banking System 54514 -82683 115008 89529 91741 2.4 Capital Account 4481192 4437226 5156447 5270609 5291812 2.5 Other items (net) 2053777 1269677 1301101 1479265 1421919 138 RBI Bulletin November 2025CURRENT STATISTICS No. 9: Liquidity Aggregates (₹ Crore) Aggregates 2024-25 2024 2025 Sep. Jul. Aug. Sep. 1 2 3 4 5 1 NM₃ 27896780 26567267 28681808 28871731 28906851 2 Postal Deposits 756787 728509 791633 798148 812817 3 L₁ ( 1 + 2) 28653567 27295776 29473441 29669879 29719668 4 Liabilities of Financial Institutions 95148 68824 113786 116169 116595 4.1 Term Money Borrowings 10 94 5 5 5 4.2 Certificates of Deposit 80810 55520 98755 100855 101105 4.3 Term Deposits 14328 13210 15027 15310 15485 5 L₂ (3 + 4) 28748715 27364600 29587228 29786048 29836262 6 Public Deposits with Non-Banking Financial Companies 121178 112512 .. .. 131730 7 L₃ (5 + 6) 28869893 27477112 .. .. 29967993 Note : Figures in the columns might not add up to the total due to rounding off of numbers. RBI Bulletin November 2025 139CURRENT STATISTICS No. 10: Reserve Bank of India Survey (₹ Crore) Item Outstanding as on March 31/last reporting Fortnights of the month/reporting Fortnights 2024-25 2024 2025 Sep. 20 Aug. 22 Sep. 5 Sep. 19 1 2 3 4 5 1 Components 1.1 Currency in Circulation 3724448 3490538 3811491 3815402 3800152 1.2 Bankers’ Deposits with the RBI 991488 1018975 993927 988435 942717 1.2.1 Scheduled Commercial Banks 926001 956255 932900 926601 884937 1.3 ‘Other’ Deposits with the RBI 113307 94085 109645 110330 116503 Reserve Money (1.1 + 1.2 + 1.3 = 2.1 + 2.2 + 2.3 – 2.4 – 2.5) 4829243 4603598 4915063 4914167 4859372 2 Sources 2.1 RBI’s Domestic Credit 1389090 897083 1294924 1321987 1273377 2.1.1 Net RBI credit to the Government 1508105 921112 1511706 1607508 1324495 2.1.1.1 Net RBI credit to the Central Government (2.1.1.1.1 + 2.1.1.1.2 + 2.1.1.1.3 + 2.1.1.1.4 – 2.1.1.1.5) 1475460 894708 1484286 1563480 1292093 2.1.1.1.1 Loans and Advances to the Central Government - - - - - 2.1.1.1.2 Investments in Treasury Bills - - - - - 2.1.1.1.3 Investments in dated Government Securities 1558574 1313609 1767876 1771131 1772839 2.1.1.1.3.1 Central Government Securities 1558574 1313609 1767876 1771131 1772839 2.1.1.1.4 Rupee Coins 329 375 501 341 151 2.1.1.1.5 Deposits of the Central Government 83443 419275 284091 207993 480898 2.1.1.2 Net RBI credit to State Governments 32646 26404 27420 44028 32403 2.1.2 RBI’s Claims on Banks -157261 -34618 -229852 -295354 -66788 2.1.2.1 Loans and Advances to Scheduled Commercial Banks -157261 -34618 -229852 -295354 -66788 2.1.3 RBI’s Credit to Commercial Sector 38246 10589 13069 9833 15669 2.1.3.1 Loans and Advances to Primary Dealers 9182 8547 10985 7758 10319 2.1.3.2 Loans and Advances to NABARD - - - - - 2.2 Government’s Currency Liabilities to the Public 36632 34594 38057 38438 38438 2.3 Net Foreign Exchange Assets of the RBI 5550947 5629250 5870992 5985014 6010871 2.3.1 Gold 668162 531633 744074 796946 817376 2.3.2 Foreign Currency Assets 4882794 5097618 5126913 5188059 5193484 2.4 Capital Account 1875114 1874081 2204951 2317742 2337264 2.5 Other Items (net) 272313 83249 83960 113529 126050 No. 11: Reserve Money - Components and Sources (₹ Crore) Item Outstanding as on March 31/last Fridays of the month/Fridays 2024-25 2024 2025 Sep. 27 Aug. 29 Sep. 5 Sep. 12 Sep. 19 Sep. 26 1 2 3 4 5 6 7 Reserve Money (1.1 + 1.2 + 1.3 = 2.1 + 2.2 + 2.3 + 2.4 + 2.5 – 2.6) 4829243 4660892 4940913 4914167 4893939 4859372 4871955 1 Components 1.1 Currency in Circulation 3724448 3482214 3802317 3815402 3815642 3800152 3798500 1.2 Bankers' Deposits with RBI 991488 1083333 1020834 988435 966963 942717 955268 1.3 ‘Other’ Deposits with RBI 113307 95346 117762 110330 111335 116503 118187 2 Sources 2.1 Net Reserve Bank Credit to Government 1508105 957345 1580652 1607508 1566808 1324495 1337046 2.2 Reserve Bank Credit to Banks -157261 -84648 -254030 -295354 -272081 -66788 -62754 2.3 Reserve Bank Credit to Commercial Sector 38246 10556 13034 9833 9841 15669 19049 2.4 Net Foreign Exchange Assets of RBI 5550947 5743253 5948355 5985014 6027742 6010871 6033575 2.5 Government's Currency Liabilities to the Public 36632 34833 38438 38438 38438 38438 38864 2.6 Net Non- Monetary Liabilities of RBI 2147427 2000447 2385536 2431271 2476809 2463314 2493824 140 RBI Bulletin November 2025CURRENT STATISTICS No. 12: Commercial Bank Survey (₹ Crore) Item Outstanding as on last reporting Fortnights of the month/ reporting Fortnights of the month 2024-25 2024 2025 Sep. 20 Aug. 22 Sep. 5 Sep. 19 1 2 3 4 5 1 Components 1.1 Aggregate Deposits of Residents 22288331 21238985 23205355 23366575 23244558 1.1.1 Demand Deposits 2698049 2498373 2889395 2891510 2861501 1.1.2 Time Deposits of Residents 19590283 18740611 20315960 20475064 20383057 1.1.2.1 Short-term Time Deposits 8815627 8433275 9142182 9213779 9172375 1.1.2.1.1 Certificates of Deposits (CDs) 527375 467853 494788 495811 495098 1.1.2.2 Long-term Time Deposits 10774655 10307336 11173778 11261285 11210681 1.2 Call/Term Funding from Financial Institutions 915248 879626 862409 816569 858523 2 Sources 2.1 Domestic Credit 26156690 24696244 26664877 26892214 27020089 2.1.1 Credit to the Government 6697298 6401753 6756507 6819538 6845762 2.1.2 Credit to the Commercial Sector 19459392 18294491 19908370 20072676 20174327 2.1.2.1 Bank Credit 18243972 17125052 18646842 18800686 18902861 2.1.2.1.1 Non-food Credit 18207441 17105126 18596386 18755105 18857577 2.1.2.2 Net Credit to Primary Dealers 15458 15588 16319 12107 15882 2.1.2.3 Investments in Other Approved Securities 630 649 459 459 519 2.1.2.4 Other Investments (in non-SLR Securities) 1199332 1153202 1244750 1259425 1255065 2.2 Net Foreign Currency Assets of Commercial Banks (2.2.1-2.2.2-2.2.3) 54514 -82683 115008 89529 91741 2.2.1 Foreign Currency Assets 529621 358411 554451 533548 535782 2.2.2 Non-resident Foreign Currency Repatriable Fixed Deposits 292270 266682 299180 302335 301953 2.2.3 Overseas Foreign Currency Borrowings 182837 174411 140262 141683 142088 2.3 Net Bank Reserves (2.3.1+2.3.2-2.3.3) 791777 1077119 1246606 1306061 1033131 2.3.1 Balances with the RBI 882415 956255 932900 926601 884937 2.3.2 Cash in Hand 81874 86246 83854 84106 81405 2.3.3 Loans and Advances from the RBI 172512 -34618 -229852 -295354 -66788 2.4 Capital Account 2581908 2538975 2927326 2928696 2930377 2.5 Other items (net) (2.1+2.2+2.3-2.4-1.1-1.2) 1217493 1033095 1031402 1175964 1111502 2.5.1 Other Demand and Time Liabilities (net of 2.2.3) 878795 856786 893063 973717 932337 2.5.2 Net Inter-Bank Liabilities (other than to PDs) 118268 131836 115916 91948 97319 No. 13: Scheduled Commercial Banks’ Investments (₹ Crore) Item As on 2024 2025 March 21, 2025 Sep. 20 Aug. 22 Sep. 05 Sep. 19 1 2 3 4 5 1 SLR Securities 6697928 6404044 6756966 6819997 6846281 2 Other Government Securities (Non-SLR) 165500 157582 161651 161293 161725 3 Commercial Paper 63163 63049 73804 72978 74766 4 Shares issued by 4.1 PSUs 13874 13615 14994 14908 15049 4.2 Private Corporate Sector 95984 97368 101330 103183 99999 4.3 Others 7664 6834 7717 7739 7440 5 Bonds/Debentures issued by 5.1 PSUs 130308 123351 133322 136193 134665 5.2 Private Corporate Sector 248138 250152 243883 245764 249218 5.3 Others 150000 146237 162102 168697 165211 6 Instruments issued by 6.1 Mutual funds 119867 111561 140363 139781 142908 6.2 Financial institutions 204865 183453 207378 208889 204084 Note: Data against column Nos. (1), (2) & (3) are Final and for column Nos. (4) & (5) data are Provisional. Data include the impact of merger of a non-bank with a bank w.e.f. July 1, 2023. RBI Bulletin November 2025 141CURRENT STATISTICS No. 14: Business in India - All Scheduled Banks and All Scheduled Commercial Banks (₹ Crore) Item As on the Last Reporting Fortnights (in case of March)/ Last Fortnights All Scheduled Banks All Scheduled Commercial Banks 2024 2025 2024 2025 2024-25 2024-25 Sep. Aug. Sep. Sep. Aug. Sep. 1 2 3 4 5 6 7 8 Number of Reporting Banks 208 208 196 196 135 135 121 121 1 Liabilities to the Banking System 458011 453177 448306 464067 451305 448404 440120 455863 1.1 Demand and Time Deposits from Banks 315675 298150 329183 346864 309414 293853 321524 339216 1.2 Borrowings from Banks 112027 131578 92202 86883 111976 131408 92173 86864 1.3 Other Demand and Time Liabilities 30310 23449 26921 30320 29916 23143 26423 29783 2 Liabilities to Others 25053097 24067308 26115123 26258642 24557481 23597567 25599553 25741907 2.1 Aggregate Deposits 23055487 22197886 24202235 24279536 22580601 21746608 23707673 23782970 2.1.1 Demand 2748263 2630676 3051908 3069014 2698049 2582832 3001393 3019116 2.1.2 Time 20307224 19567210 21150327 21210522 19882552 19163776 20706280 20763854 2.2 Borrowings 920568 905313 819580 868016 915248 900827 815149 863128 2.3 Other Demand and Time Liabilities 1077042 964109 1093308 1111091 1061632 950132 1076731 1095809 3 Borrowings from Reserve Bank 311466 33302 1950 84836 311466 33302 1950 84836 3.1 Against Usance Bills /Promissory Notes - - - - - - - - 3.2 Others 311466 33302 1950 84836 311466 33302 1950 84836 4 Cash in Hand and Balances with Reserve Bank 985044 1132310 1070393 1001948 964289 1110006 1049343 981473 4.1 Cash in Hand 84399 92085 91835 86480 81874 89559 89688 83965 4.2 Balances with Reserve Bank 900645 1040225 978558 915468 882415 1020447 959655 897509 5 Assets with the Banking System 432645 400518 435974 458259 348496 333625 348752 367841 5.1 Balances with Other Banks 273720 253200 307280 314393 215801 201534 245338 251739 5.1.1 In Current Account 13239 15512 10899 22896 10619 12452 8810 20464 5.1.2 In Other Accounts 260481 237687 296381 291497 205182 189082 236528 231274 5.2 Money at Call and Short Notice 44772 27376 33835 39992 25838 16083 15384 18667 5.3 Advances to Banks 43856 45009 30457 31135 39504 44328 29154 30082 5.4 Other Assets 70296 74933 64401 72739 67353 71681 58877 67354 6 Investment 6850574 6589114 6951875 7023504 6697928 6439289 6784782 6856164 6.1 Government Securities 6842024 6580833 6942689 7013572 6697298 6438770 6784263 6855706 6.2 Other Approved Securities 8550 8281 9186 9932 630 519 519 458 7 Bank Credit 18708286 17661371 19202618 19547197 18243972 17215335 18732093 19073295 7a Food Credit 87145 69694 99067 95969 36531 19075 47093 43995 7.1 Loans, Cash-credits and Overdrafts 18370704 17344195 18857148 19189753 17909851 16901348 18388626 18717636 7.2 Inland Bills-Purchased 76523 67186 79073 81825 74963 65696 78758 81591 7.3 Inland Bills-Discounted 222320 211015 231103 239750 221059 209905 229973 238764 7.4 Foreign Bills-Purchased 15357 15988 13301 13237 15122 15793 13088 13036 7.5 Foreign Bills-Discounted 23382 22987 21993 22632 22977 22592 21648 22268 Note: Data in column Nos. (4) & (8) are Provisional Data include the impact of merger of a non-bank with a bank w.e.f. July 1, 2023. 142 RBI Bulletin November 2025CURRENT STATISTICS No. 15: Deployment of Gross Bank Credit by Major Sectors (₹ Crore) (₹ Crore) Outstanding as on Growth(%) Mar. 21, Financial Sector 2025 2024 2025 year so far Y-o-Y Sep. 20 Aug. 22 Sep. 19 2025-26 2025 1 2 3 4 % % I. Bank Credit (II + III) 18243972 17125052 18644997 18902934 3.6 10.4 II. Food Credit 36531 19926 50456 45284 24.0 127.3 III. Non-food Credit 18207441 17105126 18594541 18857649 3.6 10.2 1. Agriculture & Allied Activities 2287061 2167287 2324719 2361593 3.3 9.0 2. Industry (Micro and Small, Medium and Large) 3935857 3801604 4002072 4080522 3.7 7.3 2.1 Micro and Small 790430 750825 898780 916188 15.9 22.0 2.2 Medium 360475 334412 367293 382327 6.1 14.3 2.3 Large 2784953 2716366 2735998 2782006 -0.1 2.4 3. Services 5161542 4736957 5137774 5219318 1.1 10.2 3.1 Transport Operators 258409 245008 266868 267295 3.4 9.1 3.2 Computer Software 32915 29760 37829 37978 15.4 27.6 3.3 Tourism, Hotels & Restaurants 83091 78753 85840 87990 5.9 11.7 3.4 Shipping 7305 7166 8923 9585 31.2 33.8 3.5 Aviation 46026 44458 45857 46286 0.6 4.1 3.6 Professional Services 195956 177730 197147 195596 -0.2 10.1 3.7 Trade 1187030 1072493 1183548 1198879 1.0 11.8 3.7.1. Wholesale Trade¹ 648619 567242 635841 640595 -1.2 12.9 3.7.2 Retail Trade 538410 505251 547706 558284 3.7 10.5 3.8 Commercial Real Estate 532757 497333 560651 574656 7.9 15.5 3.9 Non-Banking Financial Companies (NBFCs)² of which, 1635737 1529006 1574362 1589195 -2.8 3.9 3.9.1 Housing Finance Companies (HFCs) 323146 324354 318570 324965 0.6 0.2 3.9.2 Public Financial Institutions (PFIs) 228678 203257 199719 205393 -10.2 1.1 3.10 Other Services³ 1182316 1055250 1176749 1211859 2.5 14.8 4. Personal Loans 5953521 5596719 6213373 6254274 5.1 11.7 4.1 Consumer Durables 23402 23764 22921 22279 -4.8 -6.2 4.2 Housing 3010477 2845505 3108791 3132868 4.1 10.1 4.3 Advances against Fixed Deposits 141101 125694 142074 143143 1.4 13.9 4.4 Advances to Individuals against share & bonds 10080 9546 9807 9835 -2.4 3.0 4.5 Credit Card Outstanding 284366 271813 288691 281823 -0.9 3.7 4.6 Education 137456 129116 144539 147176 7.1 14.0 4.7 Vehicle Loans 622794 602367 647829 646242 3.8 7.3 4.8 Loan against gold jewellery⁴ 208735 147081 305814 316042 51.4 114.9 4.9 Other Personal Loans 1515112 1441833 1542907 1554867 2.6 7.8 5. Priority Sector (Memo) (i) Agriculture & Allied Activities⁵ 2287794 2164159 2310308 2327797 1.7 7.6 (ii) Micro & Small Enterprises⁶ 2239409 2057867 2510253 2520120 12.5 22.5 (iii) Medium Enterprises⁷ 601451 543388 604659 627019 4.3 15.4 (iv) Housing 746651 750223 945763 974644 30.5 29.9 (v) Education Loans 62825 62389 70067 70834 12.7 13.5 (vi) Renewable Energy 10325 6778 13235 14842 43.7 119.0 (vii) Social Infrastructure 1316 1124 936 939 -28.6 -16.4 (viii) Export Credit 12479 11410 12529 11755 -5.8 3.0 (ix) Others 49552 58561 44088 43466 -12.3 -25.8 (x) Weaker Sections including net PSLC- SF/MF 1820904 1711473 1861780 1873464 2.9 9.5 Notes: (1) Data are provisional. Bank credit, Food credit and Non-food credit data are based on Section-42 return, which covers all scheduled commercial banks (SCBs), while sectoral non-food credit data are based on sector-wise and industry-wise bank credit (SIBC) return, which covers select banks accounting for about 95 per cent of total non-food credit extended by all SCBs, pertaining to the last reporting Friday of the month. (2) Data since July 28, 2023 include the impact of the merger of a non-bank with a bank. 1 Wholesale trade includes food procurement credit outside the food credit consortium. 2 NBFCs include HFCs, PFIs, Microfinance Institutions (MFIs), NBFCs engaged in gold loan and others. 3 “Other Services” include Mutual Fund (MFs), Banking and Finance other than NBFCs and MFs, and other services which are not indicated elsewhere under services. 4 Since May 2024, a bank has changed the classification of a category of agricultural loan into “Loans against gold jewellery” under retail segment. 5 “Agriculture and Allied Activities” under the priority sector also include priority sector lending certificates (PSLCs). 6 “Micro and Small Enterprises” under the priority sector include credit to micro and small enterprises in industry and services sectors and also include PSLCs. 7 “Medium Enterprises” under the priority sector include credit to medium enterprises in industry and services sectors. RBI Bulletin November 2025 143CURRENT STATISTICS No. 16: Industry-wise Deployment of Gross Bank Credit (₹ Crore) Outstanding as on Growth(%) Financial 2024 2025 Y-o-Y Mar. 21, year so far Industry 2025 Sep. 20 Aug. 22 Sep. 19 2025-26 2025 1 2 3 4 % % 2 Industries (2.1 to 2.19) 3935857 3801604 4002072 4080522 3.7 7.3 2.1 Mining & Quarrying (incl. Coal) 56756 52560 56740 59321 4.5 12.9 2.2 Food Processing 219527 192363 211735 207240 -5.6 7.7 2.2.1 Sugar 28522 18789 18470 16861 -40.9 -10.3 2.2.2 Edible Oils & Vanaspati 20927 17113 20565 19696 -5.9 15.1 2.2.3 Tea 5084 6157 4994 5110 0.5 -17.0 2.2.4 Others 164994 150304 167706 165573 0.4 10.2 2.3 Beverage & Tobacco 35513 31690 36413 38047 7.1 20.1 2.4 Textiles 277267 256793 272419 275090 -0.8 7.1 2.4.1 Cotton Textiles 107227 93859 97159 97213 -9.3 3.6 2.4.2 Jute Textiles 4288 4155 4526 4708 9.8 13.3 2.4.3 Man-Made Textiles 49091 46812 48690 49552 0.9 5.9 2.4.4 Other Textiles 116661 111967 122044 123617 6.0 10.4 2.5 Leather & Leather Products 12980 12788 13340 13441 3.6 5.1 2.6 Wood & Wood Products 27826 25229 28071 28366 1.9 12.4 2.7 Paper & Paper Products 52848 49765 53843 54707 3.5 9.9 2.8 Petroleum, Coal Products & Nuclear Fuels 154178 169289 172075 185288 20.2 9.5 2.9 Chemicals & Chemical Products 267814 263369 274397 285196 6.5 8.3 2.9.1 Fertiliser 32011 32556 28943 31450 -1.8 -3.4 2.9.2 Drugs & Pharmaceuticals 88738 86061 89048 91519 3.1 6.3 2.9.3 Petro Chemicals 26892 30677 31166 32713 21.6 6.6 2.9.4 Others 120172 114075 125241 129514 7.8 13.5 2.10 Rubber, Plastic & their Products 103464 94633 104220 105424 1.9 11.4 2.11 Glass & Glassware 13443 12447 13098 13782 2.5 10.7 2.12 Cement & Cement Products 59752 59806 61279 60968 2.0 1.9 2.13 Basic Metal & Metal Product 433502 422794 450518 461069 6.4 9.1 2.13.1 Iron & Steel 300156 300521 305116 312584 4.1 4.0 2.13.2 Other Metal & Metal Product 133345 122273 145402 148485 11.4 21.4 2.14 All Engineering 240135 218183 258626 267361 11.3 22.5 2.14.1 Electronics 52862 50514 60975 63242 19.6 25.2 2.14.2 Others 187272 167669 197652 204118 9.0 21.7 2.15 Vehicles, Vehicle Parts & Transport Equipment 119057 114310 121798 129139 8.5 13.0 2.16 Gems & Jewellery 85734 91172 94068 100358 17.1 10.1 2.17 Construction 150701 141905 148352 147751 -2.0 4.1 2.18 Infrastructure 1322831 1299854 1334182 1348574 1.9 3.7 2.18.1 Power 682953 641606 707502 718461 5.2 12.0 2.18.2 Telecommunications 118940 124047 110144 106046 -10.8 -14.5 2.18.3 Roads 311219 325928 316926 319398 2.6 -2.0 2.18.4 Airports 9156 8428 7693 7761 -15.2 -7.9 2.18.5 Ports 5916 6702 5450 7847 32.6 17.1 2.18.6 Railways 13595 11940 11521 10131 -25.5 -15.2 2.18.7 Other Infrastructure 181052 181203 174944 178931 -1.2 -1.3 2.19 Other Industries 302530 292655 296896 299400 -1.0 2.3 Note: (1) Data since July 28, 2023 include the impact of the merger of a non-bank with a bank. 144 RBI Bulletin November 2025CURRENT STATISTICS No. 17: State Co-operative Banks Maintaining Accounts with the Reserve Bank of India (₹ Crore) Item As on Reporting Day 2024 2025 2024-25 Aug. 30 Jun. 13 Jun. 27 Jul. 11 Jul. 25 Aug. 08 Aug. 22 Aug. 29 1 2 3 4 5 6 7 8 9 Number of Reporting Banks 34 34 34 34 34 34 34 34 34 1 Aggregate Deposits (2.1.1.2+2.2.1.2) 146871.0 133771.9 147828.9 147839.5 147662.0 146816.8 149137.5 146904.8 146892.1 2 Demand and Time Liabilities 2.1 Demand Liabilities 2921 5.6 27177.9 26529.6 26248.7 27486.4 26588.4 26 595.4 2675 1.4 27698 .5 2.1.1 Deposits 2.1.1.1 Inter-Bank 9022.9 7554.0 7289.6 6767.4 7387.6 7217.4 7296.8 7221.0 7278.0 2.1.1.2 Others 14063.9 13721 .9 13791.0 13170.9 13463.0 13008.8 13510.1 13379.0 13202.4 2.1.2 Borrowings from Banks 700.0 721.2 1543.3 964.6 760.2 54.0 271.4 829.1 2.1.3 Other Demand Liabilities 5428.9 5902.0 4727.8 4767.0 5671.2 5602.1 5734.4 5880.1 6389.0 2.2 Time Liabilities 201100.7 181698.8 199176.8 199275.4 198798.4 198088.7 199385.4 199285.7 197195.3 2.2.1 Deposits 2.2.1.1 Inter-Bank 66874.3 59084.4 63644.4 63111.1 63174.8 62813.5 62249.7 62174.4 62001.7 2.2.1.2 Others 132807.1 120050.0 134037.9 134668.6 134199.0 133808.0 135627.4 133525.8 133689.6 2.2.2 Borrowings from Banks 643.9 1235.0 615.5 615.5 614.7 614.7 614.7 2738.9 611.7 2.2.3 Other Time Liabilities 775.4 1329.4 878.9 880.3 809.9 852.5 893.6 846.5 892.2 3 Borrowing from Reserve Bank 699.5 499.8 499.8 729.7 944.5 1153.0 1143.0 1144.5 4 Borrowings from a notified bank / Government 126928.5 84199.0 113368.7 113728.9 114754.7 114530.1 113046.3 114575.4 112260.4 4.1 Demand 53459.8 23957.2 48429.0 48853.6 51115.0 50687.4 50570.5 51208.4 49982.9 4.2 Time 73468.7 60241.8 64939.6 64875.3 63639.7 63842.7 62475.8 63367.0 62277.5 5 Cash in Hand and Balances with Reserve Bank 13390.9 11195.1 14110.1 23560.3 12644.7 12394.2 11984.9 11467.2 11065.3 5.1 Cash in Hand 1052.1 699.1 824.3 774.2 926.3 807.2 833.6 747.0 437.3 5.2 Balance with Reserve Bank 12338.8 10496.0 13285.8 22786.0 11718.4 11587.0 11151.3 10720.1 10628.0 6 Balances with Other Banks in Current Account 1656.3 1607.4 1230.4 1132.7 1244.0 1180.3 1048.6 908.6 981.1 7 Investments in Government Securities 77220.1 75232.9 80061.3 80872.4 83406.4 83374.4 84320.6 85538.8 86245.8 8 Money at Call and Short Notice 26531.1 14673.7 18248.3 19854.6 23005.0 20692.8 21553.9 21350.7 20842.1 9 Bank Credit (10.1+11) 174828.8 136830.6 173173.8 171391.3 170564.4 170198.2 170459.0 171092.8 170084.7 10 Advances 10.1 Loans, Cash-Credits and Overdrafts 174590.4 136641.1 172882.6 171119.8 170281.6 169936.2 170193.2 170854.0 169841.2 10.2 Due from Banks 12460 7.6 137902.0 116476.4 117780.4 116845.5 1 16943.7 116 598.0 11693 0.7 118007. 5 11 Bills Purchased and Discounted 238.4 189.5 291.2 271.5 282.8 261.9 265.8 238.8 243.5 RBI Bulletin November 2025 145CURRENT STATISTICS No. 18 (a): Flow of Financial Resources to Commercial Sector in India (₹ Crore) April-March Up to October 31 Source 2023-24 2024-25 2024-25 2025-26 P 1 2 3 4 5 1 Non-Food Bank Credit 21,40,243 17,98,321 9,80,394 11,12,687 2 Non-Bank Sources (2.1+2.2) 12,63,721 17,10,459 6,43,105 8,95,813 2.1 Domestic Sources 10,20,302 13,85,609 5,04,612 6,70,531 2.1.1 Equity Issuances by Non-Financial Entities 1,35,008 3,81,161 1,80,049 1,46,487 2.1.2 Corporate Bond Issuances by Non-Financial Entities 1,67,374 1,97,795 39,201 2,25,144 2.1.3 Hybrid Instruments (REITs/ InvITs) by Non-Financial Entities 39,024 31,442 9,917 6,850 2.1.4 Commercial Paper Issuances by Non-Financial Entities 19,712 18,819 79,185 78,219 2.1.5 Credit by Housing Finance Companies (Net of Bank Borrowings) 1,41,816 1,34,852 -48,506 -9,618 2.1.6 Credit by RBI-regulated All India Financial Institutions 73,386 99,501 4,617 -28,888 2.1.7 Credit by Non-Banking Financial Companies (Net of Bank Borrowings) 4,43,982 5,22,037 2,40,150 2,52,338 2.2 Foreign Sources 2,43,419 3,24,850 1,38,493 2,25,282 2.2.1 External Commercial Borrowings by Non-Financial Entities 27,916 19,201 -792 25,475 2.2.2 ADR/GDR by Non-Financial Entities 0 0 0 0 2.2.3 Short-term Credit from Abroad -6,741 58,860 18,583 6,147 2.2.4 Foreign Direct Investment to India 2,22,244 2,46,788 1,20,702 1,93,660 3 Total Flow of Resources (1+2) 34,03,964 35,08,780 16,23,499 20,08,500 P: Provisional. The coverage of data for columns 4 and 5 from Sources No.: 2.1.1, 2.1.2, 2.1.3, 2.1.5, 2.1.6, 2.1.7, 2.2.1 and 2.2.2: Up to September. 2.2.3: Up to June. 2.2.4: Up to August. Notes: i. Non-food bank credit pertains to scheduled commercial banks (SCBs) and excludes credit extended by co-operative banks. ii. Credit extended by banks, NBFCs and HFCs is inclusive of personal loans. iii. Data on all items are presented on net basis, except equity and hybrid instruments which are on gross basis. iv. All India Financial Institutions (AIFIs) include National Bank for Agriculture and Rural Development (NABARD), National Housing Bank (NHB), Small Industries D evelopment Bank of India (SIDBI), Export-Import Bank of India (EXIM Bank), and National Bank for Financing Infrastructure and Development (NaBFID). Credit e xtended by AIFIs excludes refinancing to SCBs, NBFCs, and HFCs, and direct loans to domestic and foreign governments/institutions. v. Data pertaining to HDFC Limited, which merged with HDFC Bank effective from July 1, 2023, is included under credit by Housing Finance Companies prior to its m erger while it is included under bank credit post-merger. vi. Data on credit by Housing Finance Companies (HFCs) and Non-Banking Financial Companies (NBFCs) has been adjusted for the conversion of some HFCs into N BFCs. Sources: RBI; SEBI; AIFIs; and RBI staff estimates. 146 RBI Bulletin November 2025No. 18 (b): Outstanding Credit to Commercial Sector in India Y-o-Y Growth (₹ crore) (Per cent) At End- As on October At End-March As on October 31 Source March 31 2024 2025 2024 2025 2023 2024 2025 2023 2024 2025 P over over over over 2023 2024 2023 2024 P 1 2 3 4 5 6 7 8 9 10 11 1 Non-Food Bank Credit 1,36,55,330 1,64,09,083 1,82,07,441 1,55,61,722 1,73,89,477 1,93,20,128 20.2 11.0 11.7 11.1 2 Non-Bank Sources (2.1+2.2) 74,43,091 77,56,314 88,85,434 72,07,315 81,00,470 94,90,483 4.2 14.6 12.4 17.2 2.1 Domestic Sources 53,95,038 56,59,037 66,37,411 51,58,529 59,73,684 71,54,605 4.9 17.3 15.8 19.8 Corporate Bond Issuances by Non-Financial 2.1.1 16,58,140 18,25,514 20,23,310 16,55,194 18,64,715 22,48,453 10.1 10.8 12.7 20.6 Entities Commercial Paper Issuances by Non-Financial 2.1.2 89,816 1,09,528 1,28,347 1,19,756 1,88,712 2,06,566 21.9 17.2 57.6 9.5 Entities Credit by Housing Finance Companies (Net of 2.1.3 10,39,420 5,98,965 6,27,125 5,69,598 5,50,459 6,17,507 -42.4 4.7 -3.4 12.2 Bank Borrowings) Credit by RBI-regulated All India Financial 2.1.4 3,51,224 4,24,610 5,24,111 3,21,587 4,29,227 4,95,223 20.9 23.4 33.5 15.4 Institutions Credit by Non-Banking Financial Companies (Net 2.1.5 22,56,439 27,00,421 33,34,518 24,92,394 29,40,571 35,86,856 19.7 23.5 18.0 22.0 of Bank Borrowings) 2.2 Foreign Sources 20,48,053 20,97,277 22,48,023 20,48,786 21,26,786 23,35,877 2.4 7.2 3.8 9.8 External Commercial Borrowings by Non- 2.2.1 10,29,403 10,71,240 11,33,592 10,73,224 10,81,180 12,15,787 4.1 5.8 0.7 12.4 Financial Entities 2.2.2 Short-term Credit from Abroad 10,18,650 10,26,037 11,14,432 9,75,562 10,45,606 11,20,090 0.7 8.6 7.2 7.1 3 Total Credit (1+2) 2,10,98,421 2,41,65,397 2,70,92,875 2,27,69,037 2,54,89,947 2,88,10,611 14.5 12.1 12.0 13.0 P: Provisional. The coverage of data for columns 5, 6 and 7 from Sources No.: 2.1.1, 2.1.3, 2.1.4, 2.1.5 and 2.2.1: As at end-September. 2.2.2: As at end-June. Notes: i. Non-food bank credit pertains to scheduled commercial banks (SCBs) and excludes credit extended by co-operative banks. Including credit extended by cooperative b anks (viz., urban co-operative banks, state co-operative banks, and district central co-operative banks), non-food bank credit at end-March 2023 and 2024 stood at ₹ 1,46,22,252 crore and ₹1,74,63,724 crore, respectively. Accordingly, total outstanding credit at end- March 2023 and 2024 stood at ₹2,20,65,343 crore and ₹2,52,20,038 c rore, respectively. ii. Data on non-bank sources excludes issuances of equities and hybrid instruments under domestic sources and foreign direct investment in equities under foreign sources. 2.1.2: As at end-October. iii. In case of corporate bonds, the outstanding data for end-March 2024 and 2025 are based on SEBI’s new series of data on bonds issued by financial and non-financial c orporations. The outstanding data for end-March 2023 is worked out by adjusting the flow of 2023-24 from outstanding data for end-March 2024. iv. Flows based on outstanding data may not tally with the flows provided in Table 18 (a) due to: a) Merger of HDFC Limited with HDFC Bank on July 1, 2023; b) Conversion of some Housing Finance Companies into Non-Banking Financial Companies; and c) Valuation effect in case of foreign sources. v. Data is exclusive of current and non-current trade payables representing domestic liabilities in case of non-financial non-government public and private limited companies a s data are not available. Sources: RBI; SEBI; AIFIs; and RBI staff estimates. RBI Bulletin November 2025 147CURRENT STATISTICS Prices and Production No. 19: Consumer Price Index (Base: 2012=100) Group/Sub group 2024-25 Rural Urban Combined Rural Urban Combined Oct.24 Sep.25 Oct.25 (P) Oct.24 Sep.25 Oct.25 (P) Oct.24 Sep.25 Oct.25 (P) 1 2 3 4 5 6 7 8 9 10 11 12 1 Food and beverages 198.6 205.3 201.1 206.7 199.2 198.8 214.1 206.8 206.4 209.4 202.0 201.6 1.1 Cereals and products 195.0 193.7 194.6 196.3 197.6 197.2 194.1 198.1 197.9 195.6 197.8 197.4 1.2 Meat and fish 222.3 231.9 225.7 221.6 223.9 224.3 230.5 236.1 236.5 224.7 228.2 228.6 1.3 Egg 192.8 197.5 194.6 194.1 195.6 196.6 199.0 200.4 201.8 196.0 197.5 198.6 1.4 Milk and products 186.3 187.0 186.6 186.9 191.0 190.8 187.9 192.9 193.3 187.3 191.7 191.7 1.5 Oils and fats 175.4 165.5 171.8 181.0 203.4 202.4 168.2 185.3 185.0 176.3 196.8 196.0 1.6 Fruits 188.3 194.2 191.0 192.5 209.4 208.4 196.1 211.0 205.9 194.2 210.1 207.2 1.7 Vegetables 222.1 269.6 238.2 270.5 197.3 196.7 333.9 240.9 240.2 292.0 212.1 211.5 1.8 Pulses and products 208.0 213.5 209.8 215.0 181.6 180.4 220.1 185.4 184.3 216.7 182.9 181.7 1.9 Sugar and confectionery 130.4 132.6 131.2 131.3 136.3 136.8 133.0 137.8 138.0 131.9 136.8 137.2 1.10 Spices 228.5 223.9 227.0 229.7 221.9 221.3 225.0 219.3 219.3 228.1 221.0 220.6 1.11 Non-alcoholic beverages 185.2 173.9 180.5 185.4 191.7 191.2 174.0 180.8 180.9 180.6 187.1 186.9 1.12 Prepared meals, snacks, sweets 199.4 209.7 204.2 199.6 206.8 207.2 210.2 218.4 218.8 204.5 212.2 212.6 2 Pan, tobacco and intoxicants 207.3 212.6 208.7 207.4 212.7 213.5 213.5 218.7 219.3 209.0 214.3 215.0 3 Clothing and footwear 197.9 186.7 193.5 198.3 201.8 201.4 187.1 191.3 190.8 193.9 197.6 197.2 3.1 Clothing 198.8 188.8 194.9 199.2 202.8 202.6 189.2 193.7 193.5 195.3 199.2 199.0 3.2 Footwear 192.7 174.7 185.2 192.9 195.6 194.0 175.2 178.1 175.7 185.5 188.3 186.4 4 Housing -- 181.5 181.5 -- -- -- 182.7 186.4 188.1 182.7 186.4 188.1 5 Fuel and light 181.2 169.7 176.9 181.1 184.2 183.8 169.7 174.3 174.5 176.8 180.4 180.3 6 Miscellaneous 189.3 180.7 185.1 189.9 199.7 201.4 181.5 189.8 191.0 185.8 194.9 196.4 6.1 Household goods and services 185.7 177.1 181.6 185.8 188.9 189.0 177.4 181.9 182.4 181.8 185.6 185.9 6.2 Health 198.4 193.2 196.4 198.6 206.7 206.3 193.6 201.1 200.9 196.7 204.6 204.3 6.3 Transport and communication 175.5 164.8 169.9 176.4 179.6 178.2 165.5 168.2 166.9 170.7 173.6 172.3 6.4 Recreation and amusement 180.1 175.5 177.5 180.4 183.4 182.9 176.0 179.0 178.7 177.9 180.9 180.5 6.5 Education 190.8 186.2 188.1 191.8 197.5 197.6 187.6 194.3 194.7 189.3 195.6 195.9 6.6 Personal care and effects 204.3 206.2 205.1 205.1 240.8 254.9 207.3 242.3 255.7 206.0 241.4 255.2 General Index (All Groups) 194.9 190.0 192.6 199.5 198.8 199.0 193.7 194.9 195.4 196.8 197.0 197.3 Source: National Statistical Office, Ministry of Statistics and Programme Implementation, Government of India. P: Provisional No. 20: Other Consumer Price Indices Item Base Year Linking 2024-25 2024 2025 Factor Sep. Aug. Sep. 1 2 3 4 5 6 1 Consumer Price Index for Industrial Workers 2016 2.88 142.6 143.3 147.1 147.3 2 Consumer Price Index for Agricultural Labourers 2019 9.69 - 136.3 136.3 136.2 3 Consumer Price Index for Rural Labourers 2019 9.78 - 136.0 136.6 136.4 Source: Labour Bureau, Ministry of Labour and Employment, Government of India. CPI-AL and RL indices for 2024 (Base Year 2019) are calculated using the published inflation rates. No. 21: Monthly Average Price of Gold and Silver in Mumbai Item 2024-25 2024 2025 Sep. Aug. Sep. 1 2 3 4 1 Standard Gold (₹ per 10 grams) 75842 72878 99696 109591 2 Silver (₹ per kilogram) 89131 86187 114032 129257 Source: India Bullion & Jewellers Association Ltd., Mumbai for Gold and Silver prices in Mumbai. 148 RBI Bulletin November 2025CURRENT STATISTICS No. 22: Wholesale Price Index (Base: 2011-12 = 100) Commodities Weight 2024-25 2024 2025 Oct. Aug. Sep.(P) Oct.(P) 1 2 3 4 5 6 1 ALL COMMODITIES 100.000 154.9 156.7 155.2 154.9 154.8 1.1 PRIMARY ARTICLES 22.618 192.5 200.6 191.0 189.0 188.2 1.1.1 FOOD ARTICLES 15.256 205.3 217.9 202.5 199.8 199.8 1.1.1.1 Food Grains (Cereals+Pulses) 3.462 210.1 213.4 205.1 204.4 204.4 1.1.1.2 Fruits & Vegetables 3.475 241.4 291.6 231.1 219.2 216.7 1.1.1.3 Milk 4.440 185.8 185.6 190.7 190.8 191.2 1.1.1.4 Eggs, Meat & Fish 2.402 173.4 171.0 173.2 174.8 174.0 1.1.1.5 Condiments & Spices 0.529 232.7 243.5 199.6 202.1 206.2 1.1.1.6 Other Food Articles 0.948 213.6 219.3 218.6 216.9 223.0 1.1.2 NON-FOOD ARTICLES 4.119 161.7 161.9 169.1 167.3 164.4 1.1.2.1 Fibres 0.839 161.4 160.9 167.6 168.1 168.0 1.1.2.2 Oil Seeds 1.115 181.5 185.4 203.6 202.1 196.8 1.1.2.3 Other non-food Articles 1.960 138.7 140.1 139.7 139.1 139.5 1.1.2.4 Floriculture 0.204 277.4 247.3 269.4 244.5 212.1 1.1.3 MINERALS 0.833 229.0 229.6 238.3 238.3 242.4 1.1.3.1 Metallic Minerals 0.648 219.2 219.4 230.8 230.8 235.8 1.1.3.2 Other Minerals 0.185 263.4 265.3 264.3 264.4 265.7 1.1.4 CRUDE PETROLEUM & NATURAL GAS 2.410 151.3 147.3 139.7 140.6 136.2 1.2 FUEL & POWER 13.152 150.0 148.8 143.5 143.4 145.0 1.2.1 COAL 2.138 135.6 135.5 136.1 136.1 136.1 1.2.1.1 Coking Coal 0.647 143.4 143.4 146.4 146.4 146.4 1.2.1.2 Non-Coking Coal 1.401 125.8 125.8 126.6 126.6 126.6 1.2.1.3 Lignite 0.090 232.4 229.5 208.1 208.5 209.0 1.2.2 MINERAL OILS 7.950 156.2 153.0 149.5 148.7 149.7 1.2.3 ELECTRICITY 3.064 144.1 147.4 133.3 134.9 138.8 1.3 MANUFACTURED PRODUCTS 64.231 142.6 142.9 145.0 145.2 145.1 1.3.1 MANUFACTURE OF FOOD PRODUCTS 9.122 172.0 175.9 178.6 178.8 179.0 1.3.1.1 Processing and Preserving of meat 0.134 155.7 154.5 157.9 157.9 158.7 1.3.1.2 Processing and Preserving of fish, Crustaceans, Molluscs and products thereof 0.204 144.9 149.2 146.7 150.0 151.4 1.3.1.3 Processing and Preserving of fruit and Vegetables 0.138 132.6 132.9 135.4 135.1 135.5 1.3.1.4 Vegetable and Animal oils and Fats 2.643 168.5 178.2 185.2 186.4 186.8 1.3.1.5 Dairy products 1.165 180.8 181.6 184.5 184.7 185.9 1.3.1.6 Grain mill products 2.010 186.9 188.0 187.0 186.3 185.5 1.3.1.7 Starches and Starch products 0.110 167.0 172.4 151.2 150.8 149.6 1.3.1.8 Bakery products 0.215 170.5 170.0 177.2 176.8 177.2 1.3.1.9 Sugar, Molasses & honey 1.163 139.1 139.0 144.3 143.9 144.4 1.3.1.10 Cocoa, Chocolate and Sugar confectionery 0.175 160.6 160.5 176.9 177.0 174.4 1.3.1.11 Macaroni, Noodles, Couscous and Similar farinaceous products 0.026 156.7 155.7 160.1 160.5 161.2 1.3.1.12 Tea & Coffee products 0.371 190.7 197.7 192.6 189.5 189.9 1.3.1.13 Processed condiments & salt 0.163 192.6 191.5 190.1 190.4 188.7 1.3.1.14 Processed ready to eat food 0.024 152.7 152.8 157.7 155.0 156.4 1.3.1.15 Health supplements 0.225 185.1 189.4 187.3 190.7 192.4 1.3.1.16 Prepared animal feeds 0.356 204.1 210.2 205.3 204.2 205.3 1.3.2 MANUFACTURE OF BEVERAGES 0.909 134.1 134.5 135.6 135.7 135.9 1.3.2.1 Wines & spirits 0.408 136.0 136.5 138.8 138.9 139.4 1.3.2.2 Malt liquors and Malt 0.225 138.7 138.7 140.4 140.4 140.7 1.3.2.3 Soft drinks; Production of mineral waters and Other bottled waters 0.275 127.5 128.1 126.7 127.2 126.6 1.3.3 MANUFACTURE OF TOBACCO PRODUCTS 0.514 177.8 176.0 181.7 181.1 181.6 1.3.3.1 Tobacco products 0.514 177.8 176.0 181.7 181.1 181.6 RBI Bulletin November 2025 149CURRENT STATISTICS No. 22: Wholesale Price Index (Contd.) (Base: 2011-12 = 100) Commodities Weight 2024-25 2024 2025 Oct. Aug. Sep.(P) Oct.(P) 1 2 3 4 5 6 1.3.4 MANUFACTURE OF TEXTILES 4.881 136.3 135.9 137.7 138.1 138.5 1.3.4.1 Preparation and Spinning of textile fibres 2.582 121.4 121.2 120.7 120.4 120.2 1.3.4.2 Weaving & Finishing of textiles 1.509 158.3 157.5 163.0 164.5 165.5 1.3.4.3 Knitted and Crocheted fabrics 0.193 124.0 125.5 126.7 126.7 127.9 1.3.4.4 Made-up textile articles, Except apparel 0.299 160.4 160.4 160.1 160.6 161.0 1.3.4.5 Cordage, Rope, Twine and Netting 0.098 142.7 142.2 158.9 161.1 164.3 1.3.4.6 Other textiles 0.201 134.9 134.6 133.1 133.8 134.4 1.3.5 MANUFACTURE OF WEARING APPAREL 0.814 153.4 153.9 155.8 156.2 156.5 1.3.5.1 Manufacture of Wearing Apparel (woven), Except fur Apparel 0.593 150.9 151.0 154.0 154.1 154.8 1.3.5.2 Knitted and Crocheted apparel 0.221 160.1 161.8 160.4 161.9 161.0 1.3.6 MANUFACTURE OF LEATHER AND RELATED PRODUCTS 0.535 125.3 125.7 127.8 127.3 127.3 1.3.6.1 Tanning and Dressing of leather; Dressing and Dyeing of fur 0.142 106.1 106.5 110.2 109.4 108.7 1.3.6.2 Luggage, Handbags, Saddlery and Harness 0.075 142.5 144.1 142.3 142.2 142.9 1.3.6.3 Footwear 0.318 129.7 129.9 132.2 131.7 131.9 1.3.7 MANUFACTURE OF WOOD AND PRODUCTS OF WOOD AND CORK 0.772 149.2 148.7 149.5 150.0 151.1 1.3.7.1 Saw milling and Planing of wood 0.124 141.1 142.0 141.0 142.1 143.3 1.3.7.2 Veneer sheets; Manufacture of plywood, Laminboard, Particle board and Other panels and Boards 0.493 148.6 147.6 148.3 149.0 150.3 1.3.7.3 Builder's carpentry and Joinery 0.036 215.3 216.2 215.3 215.3 215.4 1.3.7.4 Wooden containers 0.119 140.6 140.3 143.2 142.7 143.4 1.3.8 MANUFACTURE OF PAPER AND PAPER PRODUCTS 1.113 139.2 139.8 139.9 140.3 140.3 1.3.8.1 Pulp, Paper and Paperboard 0.493 144.6 144.5 143.8 144.6 145.0 1.3.8.2 Corrugated paper and Paperboard and Containers of paper and Paperboard 0.314 147.3 149.3 150.7 150.2 149.9 1.3.8.3 Other articles of paper and Paperboard 0.306 122.4 122.4 122.6 123.4 122.7 1.3.9 PRINTING AND REPRODUCTION OF RECORDED MEDIA 0.676 187.3 186.0 191.5 190.7 190.1 1.3.9.1 Printing 0.676 187.3 186.0 191.5 190.7 190.1 1.3.10 MANUFACTURE OF CHEMICALS AND CHEMICAL PRODUCTS 6.465 136.5 136.3 137.2 137.1 136.8 1.3.10.1 Basic chemicals 1.433 138.6 137.6 141.1 141.2 141.2 1.3.10.2 Fertilizers and Nitrogen compounds 1.485 143.1 142.9 143.0 143.0 143.2 1.3.10.3 Plastic and Synthetic rubber in primary form 1.001 133.6 133.9 135.0 134.2 132.8 1.3.10.4 Pesticides and Other agrochemical products 0.454 128.8 129.3 131.9 132.4 132.1 1.3.10.5 Paints, Varnishes and Similar coatings, Printing ink and Mastics 0.491 139.5 139.8 138.0 137.5 137.9 1.3.10.6 Soap and Detergents, Cleaning and Polishing preparations, Perfumes and Toilet preparations 0.612 139.7 139.5 142.8 142.5 142.1 1.3.10.7 Other chemical products 0.692 135.4 136.1 132.8 132.6 132.3 1.3.10.8 Man-made fibres 0.296 104.9 102.8 103.1 102.7 102.1 1.3.11 MANUFACTURE OF PHARMACEUTICALS, MEDICINAL CHEMICAL AND BOTANICAL PRODUCTS 1.993 144.3 143.5 145.6 145.8 146.2 1.3.11.1 Pharmaceuticals, Medicinal chemical and Botanical products 1.993 144.3 143.5 145.6 145.8 146.2 1.3.12 MANUFACTURE OF RUBBER AND PLASTICS PRODUCTS 2.299 129.0 129.6 129.2 129.0 128.9 1.3.12.1 Rubber Tyres and Tubes; Retreading and Rebuilding of Rubber Tyres 0.609 115.6 116.5 114.4 114.7 114.6 1.3.12.2 Other Rubber Products 0.272 112.1 113.4 113.9 113.1 112.6 1.3.12.3 Plastics products 1.418 138.1 138.2 138.5 138.1 138.2 1.3.13 MANUFACTURE OF OTHER NON-METALLIC MINERAL PRODUCTS 3.202 131.5 130.4 133.8 133.9 133.0 1.3.13.1 Glass and Glass products 0.295 163.2 162.5 163.0 162.2 162.8 1.3.13.2 Refractory products 0.223 121.6 118.7 123.9 123.9 123.4 1.3.13.3 Clay Building Materials 0.121 124.4 126.0 133.4 134.8 133.9 1.3.13.4 Other Porcelain and Ceramic Products 0.222 124.6 124.1 125.6 125.6 126.1 1.3.13.5 Cement, Lime and Plaster 1.645 130.4 128.8 133.5 133.7 132.0 150 RBI Bulletin November 2025CURRENT STATISTICS No. 22: Wholesale Price Index (Contd.) (Base: 2011-12 = 100) Commodities Weight 2024-25 2024 2025 Oct. Aug. Sep.(P) Oct.(P) 1 2 3 4 5 6 1.3.13.6 Articles of Concrete, Cement and Plaster 0.292 139.2 138.7 140.4 140.1 139.9 1.3.13.7 Cutting, Shaping and Finishing of Stone 0.234 134.4 135.6 138.6 139.3 140.0 1.3.13.8 Other Non-Metallic Mineral Products 0.169 95.2 94.8 92.3 91.9 91.6 1.3.14 MANUFACTURE OF BASIC METALS 9.646 139.7 139.3 137.6 137.4 137.1 1.3.14.1 Inputs into steel making 1.411 133.6 134.0 131.5 131.8 131.6 1.3.14.2 Metallic Iron 0.653 141.8 142.6 128.3 127.5 126.4 1.3.14.3 Mild Steel - Semi Finished Steel 1.274 117.9 118.0 115.9 115.4 114.6 1.3.14.4 Mild Steel -Long Products 1.081 140.4 140.0 135.2 135.6 134.4 1.3.14.5 Mild Steel - Flat products 1.144 134.2 132.5 131.9 130.8 129.2 1.3.14.6 Alloy steel other than Stainless Steel- Shapes 0.067 135.4 134.6 129.2 128.3 126.3 1.3.14.7 Stainless Steel - Semi Finished 0.924 131.1 128.3 123.9 122.4 118.4 1.3.14.8 Pipes & tubes 0.205 164.7 162.8 161.4 161.8 162.1 1.3.14.9 Non-ferrous metals incl. precious metals 1.693 157.4 157.6 164.1 164.5 167.7 1.3.14.10 Castings 0.925 144.9 144.8 143.6 143.5 143.7 1.3.14.11 Forgings of steel 0.271 172.2 172.7 174.3 175.9 173.7 1.3.15 MANUFACTURE OF FABRICATED METAL PRODUCTS, EXCEPT MACHINERY AND EQUIPMENT 3.155 136.0 135.0 137.0 136.9 137.0 1.3.15.1 Structural Metal Products 1.031 130.8 129.8 131.8 131.8 130.7 1.3.15.2 Tanks, Reservoirs and Containers of Metal 0.660 149.5 147.1 150.6 149.2 152.6 1.3.15.3 Steam generators, Except Central Heating Hot Water Boilers 0.145 109.8 112.5 113.2 113.3 113.8 1.3.15.4 Forging, Pressing, Stamping and Roll-Forming of Metal; Powder Metallurgy 0.383 138.0 138.3 135.0 133.8 131.6 1.3.15.5 Cutlery, Hand Tools and General Hardware 0.208 102.0 102.0 105.5 104.8 104.2 1.3.15.6 Other Fabricated Metal Products 0.728 144.9 143.7 146.9 148.6 148.6 1.3.16 MANUFACTURE OF COMPUTER, ELECTRONIC AND OPTICAL PRODUCTS 2.009 121.5 121.5 122.3 122.1 122.5 1.3.16.1 Electronic Components 0.402 117.9 117.1 120.7 120.9 120.8 1.3.16.2 Computers and Peripheral Equipment 0.336 134.2 135.3 130.4 129.7 129.7 1.3.16.3 Communication Equipment 0.310 146.0 145.7 147.2 147.2 147.6 1.3.16.4 Consumer Electronics 0.641 101.1 100.5 100.4 99.4 100.6 1.3.16.5 Measuring, Testing, Navigating and Control equipment 0.181 119.9 120.9 126.6 126.6 126.8 1.3.16.6 Watches and Clocks 0.076 167.9 167.7 175.0 175.2 175.0 1.3.16.7 Irradiation, Electromedical and Electrotherapeutic equipment 0.055 114.4 116.6 112.9 119.4 118.4 1.3.16.8 Optical instruments and Photographic equipment 0.008 107.4 106.8 117.5 117.5 117.9 1.3.17 MANUFACTURE OF ELECTRICAL EQUIPMENT 2.930 133.7 133.8 135.1 135.5 135.8 1.3.17.1 Electric motors, Generators, Transformers and Electricity distribution and Control apparatus 1.298 132.3 131.9 133.2 133.7 133.5 1.3.17.2 Batteries and Accumulators 0.236 141.3 141.1 144.8 144.8 144.8 1.3.17.3 Fibre optic cables for data transmission or live transmission of images 0.133 118.6 120.6 116.0 116.2 117.3 1.3.17.4 Other electronic and Electric wires and Cables 0.428 154.4 155.6 160.0 160.4 162.5 1.3.17.5 Wiring devices, Electric lighting & display equipment 0.263 118.4 118.9 117.9 117.9 118.6 1.3.17.6 Domestic appliances 0.366 131.8 131.7 130.9 131.3 131.5 1.3.17.7 Other electrical equipment 0.206 123.4 123.8 125.6 126.9 126.0 1.3.18 MANUFACTURE OF MACHINERY AND EQUIPMENT 4.789 130.8 130.8 132.7 132.5 132.6 1.3.18.1 Engines and Turbines, Except aircraft, Vehicle and Two wheeler engines 0.638 132.8 133.9 136.9 137.3 138.2 1.3.18.2 Fluid power equipment 0.162 134.5 134.1 134.8 134.7 134.7 1.3.18.3 Other pumps, Compressors, Taps and Valves 0.552 118.5 118.4 120.5 120.6 120.7 1.3.18.4 Bearings, Gears, Gearing and Driving elements 0.340 128.5 127.0 130.3 130.1 131.0 1.3.18.5 Ovens, Furnaces and Furnace burners 0.008 86.6 86.3 86.6 86.6 87.5 1.3.18.6 Lifting and Handling equipment 0.285 130.0 129.6 130.7 130.7 131.0 RBI Bulletin November 2025 151CURRENT STATISTICS No. 22: Wholesale Price Index (Concld.) (Base: 2011-12 = 100) Commodities Weight 2024-25 2024 2025 Oct. Aug. Sep.(P) Oct.(P) 1 2 3 4 5 6 1.3.18.7 Office machinery and Equipment 0.006 130.2 130.2 130.2 130.2 130.2 1.3.18.8 Other general-purpose machinery 0.437 145.3 146.8 142.4 140.9 140.9 1.3.18.9 Agricultural and Forestry machinery 0.833 145.5 145.3 146.4 145.6 145.5 1.3.18.10 Metal-forming machinery and Machine tools 0.224 123.2 123.1 127.6 127.6 127.6 1.3.18.11 Machinery for mining, Quarrying and Construction 0.371 89.8 89.2 92.9 92.9 93.0 1.3.18.12 Machinery for food, Beverage and Tobacco processing 0.228 126.1 126.1 127.0 127.0 126.4 1.3.18.13 Machinery for textile, Apparel and Leather production 0.192 141.4 141.0 147.5 146.7 147.4 1.3.18.14 Other special-purpose machinery 0.468 144.9 144.3 147.8 147.9 147.6 1.3.18.15 Renewable electricity generating equipment 0.046 69.2 68.6 69.4 69.3 69.3 1.3.19 MANUFACTURE OF MOTOR VEHICLES, TRAILERS AND SEMI-TRAILERS 4.969 129.9 129.5 130.7 130.6 130.4 1.3.19.1 Motor vehicles 2.600 130.6 129.9 131.2 131.1 130.3 1.3.19.2 Parts and Accessories for motor vehicles 2.368 129.1 129.2 130.2 130.0 130.5 1.3.20 MANUFACTURE OF OTHER TRANSPORT EQUIPMENT 1.648 145.2 145.1 151.7 152.1 152.1 1.3.20.1 Building of ships and Floating structures 0.117 180.5 177.9 190.7 190.7 190.7 1.3.20.2 Railway locomotives and Rolling stock 0.110 108.9 108.1 111.0 110.3 110.7 1.3.20.3 Motor cycles 1.302 146.0 146.3 152.8 153.4 153.3 1.3.20.4 Bicycles and Invalid carriages 0.117 134.9 133.3 138.0 137.8 137.8 1.3.20.5 Other transport equipment 0.002 163.2 164.5 165.6 165.9 166.5 1.3.21 MANUFACTURE OF FURNITURE 0.727 160.3 160.9 163.5 164.5 164.5 1.3.21.1 Furniture 0.727 160.3 160.9 163.5 164.5 164.5 1.3.22 OTHER MANUFACTURING 1.064 183.8 184.1 227.7 236.6 236.2 1.3.22.1 Jewellery and Related articles 0.996 185.4 185.6 232.0 241.5 241.0 1.3.22.2 Musical instruments 0.001 201.9 199.7 203.5 198.3 205.4 1.3.22.3 Sports goods 0.012 164.9 168.0 172.4 172.7 172.7 1.3.22.4 Games and Toys 0.005 163.1 162.8 164.6 164.8 166.8 1.3.22.5 Medical and Dental instruments and Supplies 0.049 158.6 158.6 160.9 160.9 162.1 2 FOOD INDEX 24.378 192.9 202.2 193.5 192.0 192.0 Source: Office of the Economic Adviser, Ministry of Commerce and Industry, Government of India. 152 RBI Bulletin November 2025CURRENT STATISTICS No. 23: Index of Industrial Production (Base:2011-12=100) Industry Weight 2023-24 2024-25 April-September September 2024-25 2025-26 2024 2025 1 2 3 4 5 6 7 General Index 100.00 146.7 152.6 149.4 153.9 146.9 152.8 1 Sectoral Classification 1.1 Mining 14.37 128.9 132.8 122.9 120.6 111.7 111.2 1.2 Manufacturing 77.63 144.7 150.6 147.3 153.3 147.2 154.3 1.3 Electricity 7.99 198.3 208.6 217.3 219.5 206.9 213.3 2 Use-Based Classification 2.1 Primary Goods 34.05 147.7 153.5 150.4 150.7 141.3 143.3 2.2 Capital Goods 8.22 106.6 112.6 108.3 116.4 116.5 122.0 2.3 Intermediate Goods 17.22 157.3 164.0 161.2 169.7 160.8 169.4 2.4 Infrastructure/ Construction Goods 12.34 176.3 188.2 182.6 198.6 178.8 197.6 2.5 Consumer Durables 12.84 118.6 128.0 127.7 133.9 132.9 146.5 2.6 Consumer Non-Durables 15.33 153.7 151.4 147.5 144.2 145.7 141.5 Source : Central Statistics Office, Ministry of Statistics and Programme Implementation, Government of India. Government Accounts and Treasury Bills No. 24: Union Government Accounts at a Glance (₹ Crore) Financial Year April – September 2025-26 Percentage to Budget Item (Budget 2025-26 2024-25 Estimates (Actuals) (Actuals) Estimates) 2025-26 2024-25 1 2 3 4 5 1 Revenue Receipts 3420409 1695446 1622373 49.6 51.8 1.1 Tax Revenue (Net) 2837409 1229370 1265159 43.3 49.0 1.2 Non-Tax Revenue 583000 466076 357214 79.9 65.5 2 Non Debt Capital Receipt 76000 34770 14601 45.8 18.7 2.1 Recovery of Loans 29000 11353 11434 39.1 40.8 2.2 Other Receipts 47000 23417 3167 49.8 6.3 3 Total Receipts (excluding borrowings) (1+2) 3496409 1730216 1636974 49.5 51.0 4 Revenue Expenditure 3944255 1722593 1696528 43.7 45.7 of which : 4.1 Interest Payments 1276338 578182 515010 45.3 44.3 5 Capital Expenditure 1121090 580746 414966 51.8 37.3 6 Total Expenditure (4+5) 5065345 2303339 2111494 45.5 43.8 7 Revenue Deficit (4-1) 523846 27147 74155 5.2 12.8 8 Fiscal Deficit (6-3) 1568936 573123 474520 36.5 29.4 9 Gross Primary Deficit (8-4.1) 292598 -5059 -40490 -1.7 -9.0 Source: Controller General of Accounts (CGA), Ministry of Finance, Government of India and Union Budget 2025-26. RBI Bulletin November 2025 153CURRENT STATISTICS No. 25: Treasury Bills – Ownership Pattern (₹ Crore) 2024-25 2024 2025 Item Sep. 27 Aug. 22 Aug. 29 Sep. 5 Sep. 12 Sep. 19 Sep. 26 1 2 3 4 5 6 7 8 1 91-day 1.1 Banks 26554 5662 10311 11578 13541 12239 11828 11815 1.2 Primary Dealers 25258 5698 12215 15291 14848 14424 16120 19755 1.3 State Governments 40315 69688 74688 73688 70688 76482 77082 73862 1.4 Others 115688 90440 111174 108331 107811 110236 110952 108330 2 182-day 2.1 Banks 44887 44787 55520 53477 55827 56846 56680 54592 2.2 Primary Dealers 62218 30674 49746 48560 44242 41351 37969 41109 2.3 State Governments 11078 13909 19281 19330 20230 19080 17930 17780 2.4 Others 104994 78239 87634 84863 80830 76702 74252 67199 3 364-day 3.1 Banks 72304 84526 76032 75805 73736 73051 72583 69483 3.2 Primary Dealers 86939 122233 78405 76472 75967 75101 72827 74274 3.3 State Governments 37389 28145 45548 46601 46503 46231 48041 48138 3.4 Others 162757 173241 149263 150423 152005 152548 160290 167943 4 14-day Intermediate 4.1 Banks 4.2 Primary Dealers 4.3 State Governments 188072 167512 170177 155211 95736 134084 122747 121170 4.4 Others 572 449 871 673 358 558 491 1252 Total Treasury Bills (Excluding 14 day 790381 747242 769817 764420 756230 754293 756554 754280 Intermediate T Bills) # # 14D intermediate T-Bills are non-marketable unlike 91D, 182D and 364D T-Bills. These bills are ‘intermediate’ by nature as these are liquidated to replenish shortfall in the daily minimum cash balances of State Governments. Note: Primary Dealers (PDs) include banks undertaking PD business. No. 26: Auctions of Treasury Bills (Amount in ₹ Crore) Date of Notified Bids Received Bids Accepted Total Cut- Implicit Yield Auction Amount Total Face Value Total Face Value Issue off at Cut-off Price Number Number (6+7) Price (per cent) Competitive Non- Competitive Non- ( ₹ ) Competitive Competitive 1 2 3 4 5 6 7 8 9 10 91-day Treasury Bills 2025-26 Aug. 28 10000 93 22227 2321 55 9979 2321 12300 98.65 5.5087 Sep. 3 10000 117 28502 1530 46 9970 1530 11500 98.65 5.5095 Sep. 10 10000 116 27611 13019 66 9975 13019 22994 98.65 5.5050 Sep. 17 10000 141 29663 7921 64 9979 7921 17900 98.65 5.4976 Sep. 24 10000 114 44390 8304 22 9976 8304 18280 98.65 5.4749 182-day Treasury Bills 2025-26 Aug. 28 6000 89 18583 1811 43 5989 1811 7800 97.28 5.6001 Sep. 3 6000 77 17401 2616 37 5984 2616 8600 97.27 5.6198 Sep. 10 6000 91 14244 1011 52 5989 1011 7000 97.28 5.6174 Sep. 17 6000 73 20776 12 23 5988 12 6000 97.28 5.6045 Sep. 24 6000 100 23707 16 9 5984 16 6000 97.29 5.5776 364-day Treasury Bills 2025-26 Aug. 28 5000 75 12673 2176 52 4989 2176 7165 94.68 5.6397 Sep. 3 5000 61 10115 1249 42 4977 1249 6226 94.65 5.6699 Sep. 10 5000 76 13660 1481 47 4980 1481 6462 94.65 5.6689 Sep. 17 5000 96 20901 1823 10 4988 1823 6811 94.68 5.6349 Sep. 24 5000 132 26174 1110 37 4987 1110 6096 94.70 5.6080 154 RBI Bulletin November 2025CURRENT STATISTICS Financial Markets No. 27: Daily Call Money Rates (Per cent per annum) Range of Rates Weighted Average Rates As on Borrowings/ Lendings Borrowings/ Lendings 1 2 September 01 ,2025 4.75-5.55 5.42 September 02 ,2025 4.75-5.50 5.39 September 03 ,2025 4.75-5.40 5.35 September 04 ,2025 4.75-5.50 5.37 September 05 ,2025 4.90-5.60 5.07 September 06 ,2025 4.75-5.00 4.99 September 09 ,2025 4.75-5.60 5.35 September 10 ,2025 4.75-5.40 5.34 September 11 ,2025 4.75-5.40 5.35 September 12 ,2025 4.75-5.50 5.43 September 15 ,2025 4.75-5.55 5.43 September 16 ,2025 4.75-5.50 5.43 September 17 ,2025 4.75-5.65 5.47 September 18 ,2025 4.75-5.65 5.55 September 19 ,2025 4.75-5.60 5.51 September 20 ,2025 4.75-5.50 5.36 September 22 ,2025 4.75-5.80 5.58 September 23 ,2025 4.75-5.70 5.59 September 24 ,2025 4.75-5.60 5.52 September 25 ,2025 4.75-5.75 5.58 September 26 ,2025 4.75-5.75 5.59 September 29 ,2025 4.75-5.95 5.53 September 30 ,2025 4.75-5.95 5.66 October 01 ,2025 4.75-5.45 5.37 October 03 ,2025 4.75-5.45 5.36 October 04 ,2025 4.75-5.24 5.02 October 06 ,2025 4.75-5.40 5.34 October 07 ,2025 4.85-5.40 5.35 October 08 ,2025 4.75-5.40 5.34 October 09 ,2025 4.75-6.00 5.51 October 10 ,2025 4.75-5.75 5.58 October 13 ,2025 4.75-5.60 5.47 October 14 ,2025 4.85-5.50 5.39 October 15 ,2025 4.75-5.60 5.37 Note: Includes Notice Money. RBI Bulletin November 2025 155CURRENT STATISTICS No. 28: Certificates of Deposit 2024 2025 Item Oct. 18 Sep. 19 Oct. 3 Oct. 17 Oct. 31 1 2 3 4 5 1 Amount Outstanding (₹ Crore) 484133.94 501817.39 497940.28 502668.21 514877.08 1.1 Issued during the fortnight (₹ Crore) 33814.44 71730.10 38003.69 24607.72 24530.39 2 Rate of Interest (per cent) 6.93-7.65 5.49-6.82 5.49-6.82 5.50-6.40 5.76-6.46 No. 29: Commercial Paper Item 2024 2025 Oct. 31 Sep. 15 Sep. 30 Oct. 15 Oct. 31 1 2 3 4 5 1 Amount Outstanding (₹ Crore) 445104.90 526704.30 488262.80 495678.60 479629.50 1.1 Reported during the fortnight (₹ Crore) 66159.85 99422.20 70288.00 30794.65 52397.55 2 Rate of Interest (per cent) 6.99-12.53 5.72-11.97 5.73-12.34 5.71-12.49 5.79-14.93 No. 30: Average Daily Turnover in Select Financial Markets (₹ Crore) Item 2024-25 2024 2025 Sep. 27 Aug. 22 Aug. 29 Sep. 5 Sep. 12 Sep. 19 Sep. 26 1 2 3 4 5 6 7 8 1 Call Money 18990 18628 29137 27549 20066 31808 34205 31298 2 Notice Money 2506 208 403 9328 6430 426 7384 656 3 Term Money 941 805 1419 1360 967 2350 1259 1038 4 Triparty Repo 692068 678781 706895 880736 559054 681101 857666 738792 5 Market Repo 578912 548987 664441 776484 510321 623329 811438 681777 6 Repo in Corporate Bond 5212 5566 11865 12741 7895 11061 14021 15960 7 Forex (US $ million) 131877 140598 113060 143071 103733 128636 125999 140364 8 Govt. of India Dated Securities 56065 156651 100674 113169 86539 181150 103508 131047 9 State Govt. Securities 3971 11412 8004 6971 5486 5944 9797 7932 10 Treasury Bills 10.1 91-Day 2514 2522 5889 4381 6080 8549 4842 4180 10.2 182-Day 2218 5341 3716 4324 2411 2859 2686 1619 10.3 364-Day 1854 4387 1173 2150 756 2674 5020 3423 10.4 Cash Management Bills 0 0 0 0 0 0 0 11 Total Govt. Securities (8+9+10) 66622 180314 119456 130995 101273 201176 125854 148202 11.1 RBI 1715 586 476 1196 366 1139 384 1619 156 RBI Bulletin November 2025CURRENT STATISTICS No. 31: New Capital Issues by Non-Government Public Limited Companies (Amount in ₹ Crore) 2024-25 2024-25 (Apr.-Sep.) 2025-26 (Apr.-Sep.) * Sep. 2024 Sep. 2025 * Security & Type of Issue No. of Amount No. of Amount No. of Amount No. of Amount No. of Amount Issues Issues Issues Issues Issues 1 2 3 4 5 6 7 8 9 10 1 Equity Shares 464 210190 255 85980 257 85682 58 16883 66 12914 1.1 Public 322 190478 186 74274 187 71173 47 16213 53 11312 1.2 Rights 142 19712 69 11706 70 14509 11 671 13 1602 2 Public Issue of 43 8149 21 4856 22 4998 5 1695 1 200 Bonds/ Debentures 3 Total (1+2) 507 218339 276 90836 279 90681 63 18579 67 13114 3.1 Public 365 198627 207 79130 209 76171 52 17908 54 11512 3.2 Rights 142 19712 69 11706 70 14509 11 671 13 1602 Note : 1. Since April 2020, monthly data on equity issues is compiled on the basis of their listing date. 2. Figures in the columns might not add up to the total due to rounding off numbers. 3. The table covers only public and rights issuances of equity and debt. It does not include data on private placement of debt, qualified institutional placements and preferential allotments. Source : Securities and Exchange Board of India. * : Data is Provisional RBI Bulletin November 2025 157CURRENT STATISTICS External Sector No. 32: Foreign Trade 2024 2025 2024-25 Item Unit Sep. May Jun. Jul. Aug. Sep. 1 2 3 4 5 6 7 1 Exports ₹ Crore 3703412 285611 326309 300411 318986 305471 321209 US $ Million 437705 34079 38304 34971 37042 34904 36368 1.1 Oil ₹ Crore 535157 36053 46329 38272 35754 37531 43637 US $ Million 63383 4302 5438 4455 4152 4288 4941 1.2 Non-oil ₹ Crore 3168255 249558 279979 262138 283232 267941 277572 US $ Million 374321 29777 32865 30515 32890 30615 31427 2 Imports ₹ Crore 6089909 492119 518424 464624 556093 539017 605253 US $ Million 720241 58720 60855 54087 64576 61589 68528 2.1 Oil ₹ Crore 1570226 124916 125636 118531 134077 116081 123939 US $ Million 185779 14905 14748 13798 15570 13264 14032 2.2 Non-oil ₹ Crore 4519683 367202 392788 346094 422016 422936 481314 US $ Million 534462 43815 46108 40289 49006 48325 54495 3 Trade Balance ₹ Crore -2386497 -206507 -192116 -164214 -237107 -233545 -284044 US $ Million -282537 -24640 -22552 -19116 -27534 -26685 -32160 3.1 Oil ₹ Crore -1035069 -88863 -79307 -80258 -98323 -78550 -80302 US $ Million -122396 -10603 -9309 -9343 -11418 -8975 -9092 3.2 Non-oil ₹ Crore -1351428 -117645 -112809 -83955 -138784 -154995 -203742 US $ Million -160141 -14037 -13242 -9773 -16116 -17710 -23068 Note: Data in the table are provisional. Source: Directorate General of Commercial Intelligence and Statistics. No. 33: Foreign Exchange Reserves 2024 2025 Item Unit Nov. 01 Sep. 26 Oct. 03 Oct. 10 Oct. 17 Oct. 24 Oct. 31 1 2 3 4 5 6 7 1 Total Reserves ₹ Crore 5735915 6211881 6214364 6188784 6177971 6108299 6123031 US $ Million 682130 700236 699960 697784 702280 695355 689733 1.1 Foreign Currency Assets ₹ Crore 4959943 5160999 5128990 5074094 5017935 4976853 5012117 US $ Million 589849 581757 577708 572103 570411 566548 564591 1.2 Gold ₹ Crore 586521 842935 876896 907894 954886 927080 903062 US $ Million 69751 95017 98770 102365 108546 105536 101726 Volume (Metric Tonnes) 867.79 880.18 880.18 880.18 880.18 880.18 880.18 1.3 SDRs SDRs Million 13702 13709 13709 13709 13709 13709 13709 ₹ Crore 153198 166683 167033 165714 164696 163953 165514 US $ Million 18219 18789 18814 18684 18722 18664 18644 1.4 Reserve Tranche Position in IMF ₹ Crore 36254 41264 41446 41083 40454 40412 42338 US $ Million 4311 4673 4669 4632 4602 4608 4772 * Difference, if any, is due to rounding off. Note: Exclude investment in foreign currency denominated bonds issued by IIFC (UK), SDRs transferred by Government of India to RBI, foreign currency received under SAARC and ACU currency swap arrangements and RBI’s contribution to funding of Nexus Global Payments. Foreign currency assets in US dollar take into account appreciation/depreciation of non-US currencies (such as Euro, Sterling, Yen and Australian Dollar) held in reserves. Foreign exchange holdings are converted into rupees at rupee-US dollar RBI holding rates. No. 34: Non-Resident Deposits (US $ Million) Scheme Outstanding Flows 2024 2025 2024-25 2025-26 2024-25 Sep. Aug. Sep. (P) Apr.-Sep. Apr.-Sep.(P) 1 2 3 4 5 6 1 NRI Deposits 164677 161623 166805 165928 10192 6068 1.1 FCNR(B) 32809 31080 33430 33500 5347 690 1.2 NR(E)RA 100733 100924 101204 100278 2651 3205 1.3 NRO 31135 29619 32172 32150 2195 2173 P: Provisional. 158 RBI Bulletin November 2025CURRENT STATISTICS No. 35: Foreign Investment Inflows (US $ Million) 2024-25 2025-26 (P) 2024 (P) 2025 (P) Item 2024-25 Apr.-Sep. Apr.-Sep. Sep. Aug. Sep. 1 2 3 4 5 6 1.1 Net Foreign Direct Investment (1.1.1-1.1.2) 959 3403 7642 -1175 -622 -2376 1.1.1 Direct Investment to India (1.1.1.1-1.1.1.2) 29130 15573 23961 1124 1121 1406 1.1.1.1 Gross Inflows/Gross Investments 80615 43366 50362 6332 6049 6602 1.1.1.1.1 Equity 50993 30247 36203 4084 3812 4213 1.1.1.1.1.1 Government (SIA/FIPB) 2208 380 1545 3 141 32 1.1.1.1.1.2 RBI 34686 20624 26209 2487 2795 3520 1.1.1.1.1.3 Acquisition of shares 13124 8787 7426 1516 798 582 1.1.1.1.1.4 Equity capital of unincorporated bodies 975 457 1023 78 78 78 1.1.1.1.2 Reinvested earnings 22759 10660 11311 1812 1812 1812 1.1.1.1.3 Other capital 6863 2459 2849 436 425 578 1.1.1.2 Repatriation/Disinvestment 51486 27794 26401 5207 4928 5196 1.1.1.2.1 Equity 49525 26689 25343 5005 4701 4899 1.1.1.2.2 Other capital 1960 1104 1058 202 227 297 1.1.2 Foreign Direct Investment by India 28171 12170 16320 2300 1742 3782 (1.1.2.1+1.1.2.2+1.1.2.3-1.1.2.4) 1.1.2.1 Equity capital 16945 7311 9769 881 926 2627 1.1.2.2 Reinvested Earnings 6846 3423 3595 571 571 571 1.1.2.3 Other Capital 7955 3131 4356 1031 357 935 1.1.2.4 Repatriation/Disinvestment 3575 1695 1400 183 112 351 1.2 Net Portfolio Investment (1.2.1+1.2.2+1.2.3-1.2.4) 3564 20795 -4454 9712 -2595 -743 1.2.1 GDRs/ADRs - - - - - - 1.2.2 FIIs 3283 20712 -3305 9701 -2515 -787 1.2.3 Offshore funds and others - - - - - - 1.2.4 Portfolio investment by India -281 -83 1150 -12 80 -44 1 Foreign Investment Inflows 4523 24198 3188 8537 -3217 -3120 P: Provisional No. 36: Outward Remittances under the Liberalised Remittance Scheme (LRS) for Resident Individuals (US $ Million) 2024 2025 Item 2024-25 Sep. Jul. Aug. Sep. 1 2 3 4 5 1 Outward Remittances under the LRS 29563.12 2758.25 2452.93 2642.91 2782.34 1.1 Deposit 705.26 43.00 46.24 42.75 50.75 1.2 Purchase of immovable property 322.82 25.47 39.48 36.02 42.44 1.3 Investment in equity/debt 1698.94 135.08 156.19 152.18 278.80 1.4 Gift 2938.69 221.67 223.53 190.43 195.09 1.5 Donations 11.81 0.87 0.73 0.78 0.64 1.6 Travel 16964.57 1713.06 1445.34 1618.81 1664.82 1.7 Maintenance of close relatives 3722.03 281.24 298.11 272.05 273.65 1.8 Medical Treatment 81.19 7.89 6.26 3.99 4.18 1.9 Studies Abroad 2918.91 320.10 229.25 319.17 264.34 1.10 Others 198.90 9.88 7.80 6.73 7.63 RBI Bulletin November 2025 159CURRENT STATISTICS No. 37: Indices of Nominal Effective Exchange Rate (NEER) and Real Effective Exchange Rate (REER) of the Indian Rupee 2024 2025 2023-24 2024-25 Oct. Sep. Oct. Item 1 2 3 4 5 40-Currency Basket (Base: 2015-16=100) 1 Trade-Weighted 1.1 NEER 90.75 91.01 90.85 84.54 84.58 1.2 REER 103.71 105.24 107.27 97.40 97.47 2 Export-Weighted 2.1 NEER 93.13 93.52 93.46 86.34 86.46 2.2 REER 101.22 102.34 104.33 94.45 94.55 6-Currency Basket (Trade-weighted) 1 Base : 2015-16 =100 1.1 NEER 83.62 82.38 81.98 76.38 76.59 1.2 REER 101.66 102.72 104.30 95.63 95.98 2 Base : 2022-23 =100 2.1 NEER 97.31 95.87 95.41 88.89 89.13 2.2 REER 99.86 100.90 102.45 93.94 94.28 Note: Data for 2024-25 and 2025-26 so far is provisional. 160 RBI Bulletin November 2025CURRENT STATISTICS No. 38: External Commercial Borrowings (ECBs) – Registrations (Amount in US $ Million) Item 2024-25 2024 2025 Sep. Aug. Sep. 1 2 3 4 1 Automatic Route 1.1 Number 1328 95 96 127 1.2 Amount 47800 3776 2217 2393 2 Approval Route 2.1 Number 51 1 2 1 2.2 Amount 13384 1065 1050 406 3 Total (1+2) 3.1 Number 1379 96 98 128 3.2 Amount 61184 4841 3267 2799 4 Weighted Average Maturity (in years) 5.05 4.40 5.50 5.00 5 Interest Rate (per cent) 5.1 Weighted Average Margin over alternative reference rate (ARR) for Floating Rate Loans@ 1.48 1.36 1.41 1.27 5.2 Interest rate range for Fixed Rate Loans 0.00-11.67 0.00-11.00 0.00-10.50 0.00-10.00 Borrower Category I. Corporate Manufacturing 13900 378 207 1097 II. Corporate-Infrastructure 15462 328 698 377 a.) Transport 614 0 26 215 b.) Energy 6900 15 198 3 c.) Water and Sanitation 28 0 0 0 d.) Communication 13 0 0 0 e.) Social and Commercial Infrastructure 184 0 0 0 f.) Exploration,Mining and Refinery 5356 313 470 100 g.) Other Sub-Sectors 2367 0 4 59 III. Corporate Service-Sector 3226 437 498 273 IV. Other Entities 1026 0 0 0 a.) units in SEZ 26 0 0 0 b.) SIDBI 0 0 0 0 c.) Exim Bank 1000 0 0 0 V. Banks 0 0 0 0 VI. Financial Institution (Other than NBFC ) 0 0 0 0 VII. NBFCs 26318 3578 1853 1051 a). NBFC- IFC/AFC 12389 2777 467 528 b). NBFC-MFI 459 31 0 67 c). NBFC-Others 13470 770 1386 456 VIII. Non-Government Organization (NGO) 0 0 0 0 IX. Micro Finance Institution (MFI) 0 0 0 0 X. Others 1252 120 11 1 Note: Based on applications for ECB/Foreign Currency Convertible Bonds (FCCBs) which have been allotted loan registration number during the period. @ With effect from July 01, 2023, the benchmark rate is changed to Alternative Reference Rate (ARR) RBI Bulletin November 2025 161CURRENT STATISTICS No. 39: India’s Overall Balance of Payments (US$ Million) Apr-Jun 2024 Apr-Jun 2025 (P) Credit Debit Net Credit Debit Net Item 1 2 3 4 5 6 Overall Balance Of Payments (1+2+3) 507129 501903 5226 545826 541318 4508 1 Current Account (1.1+ 1.2) 241832 250508 -8676 256736 259106 -2370 1.1 Merchandise 111158 174963 -63805 113087 181551 -68464 1.2 Invisibles (1.2.1+1.2.2+1.2.3) 130674 75545 55129 143649 77555 66094 1.2.1 Services 88465 48784 39681 97428 49507 47920 1.2.1.1 Travel 7352 9171 -1819 5855 9085 -3230 1.2.1.2 Transportation 8506 8609 -103 7708 8395 -687 1.2.1.3 Insurance 903 593 310 926 613 313 1.2.1.4 G.n.i.e. 161 309 -147 134 323 -188 1.2.1.5 Miscellaneous 71542 30102 41440 82805 31092 51712 1.2.1.5.1 Software Services 41926 4479 37447 47324 5853 41471 1.2.1.5.2 Business Services 23000 16625 6375 29511 15868 13643 1.2.1.5.3 Financial Services 2215 1267 948 1945 703 1242 1.2.1.5.4 Communication Services 519 444 75 503 380 123 1.2.2 Transfers 29520 3215 26304 34048 3029 31019 1.2.2.1 Official 18 312 -293 20 217 -197 1.2.2.2 Private 29502 2904 26598 34028 2812 31216 1.2.3 Income 12689 23546 -10857 12174 25019 -12845 1.2.3.1 Investment Income 10552 22568 -12016 10044 23992 -13948 1.2.3.2 Compensation of Employees 2137 978 1159 2130 1027 1103 2 Capital Account (2.1+2.2+2.3+2.4+2.5) 264502 251395 13107 289090 281389 7700 2.1 Foreign Investment (2.1.1+2.1.2) 183768 176600 7168 173561 166248 7313 2.1.1 Foreign Direct Investment 23925 17701 6224 27222 21517 5705 2.1.1.1 In India 22777 12171 10606 26607 12476 14131 2.1.1.1.1 Equity 16402 11673 4728 19418 12078 7340 2.1.1.1.2 Reinvested Earnings 5225 5225 5876 5876 2.1.1.1.3 Other Capital 1151 498 653 1313 398 914 2.1.1.2 Abroad 1147 5529 -4382 615 9041 -8426 2.1.1.2.1 Equity 1147 2728 -1580 615 4752 -4137 2.1.1.2.2 Reinvested Earnings 0 1712 -1712 0 1884 -1884 2.1.1.2.3 Other Capital 0 1090 -1090 0 2405 -2405 2.1.2 Portfolio Investment 159844 158899 945 146339 144731 1608 2.1.2.1 In India 159240 158343 897 145201 142720 2481 2.1.2.1.1 FIIs 159240 158343 897 145201 142720 2481 2.1.2.1.1.1 Equity 139824 140833 -1009 123636 118245 5391 2.1.2.1.1.2 Debt 19416 17510 1906 21565 24475 -2910 2.1.2.1.2 ADR/GDRs 0 0 0 0 0 0 2.1.2.2 Abroad 604 556 48 1138 2011 -872 2.2 Loans (2.2.1+2.2.2+2.2.3) 31815 26686 5129 65326 59337 5989 2.2.1 External Assistance 3640 2267 1373 3120 2398 722 2.2.1.1 By India 6 26 -20 6 11 -5 2.2.1.2 To India 3634 2241 1393 3114 2387 727 2.2.2 Commercial Borrowings 12627 11098 1529 46754 42206 4548 2.2.2.1 By India 4138 4255 -117 36024 35153 871 2.2.2.2 To India 8489 6843 1646 10730 7053 3677 2.2.3 Short Term to India 15548 13321 2228 15453 14734 719 2.2.3.1 Buyers' credit & Suppliers' Credit >180 days 13729 13321 408 15453 13689 1764 2.2.3.2 Suppliers' Credit up to 180 days 1820 0 1820 0 1045 -1045 2.3 Banking Capital (2.3.1+2.3.2) 36380 33511 2870 33634 35189 -1555 2.3.1 Commercial Banks 36259 33511 2749 33625 35189 -1564 2.3.1.1 Assets 10705 13570 -2865 8579 13083 -4504 2.3.1.2 Liabilities 25554 19941 5614 25046 22106 2939 2.3.1.2.1 Non-Resident Deposits 23426 19401 4025 23778 20164 3614 2.3.2 Others 121 0 121 10 0 10 2.4 Rupee Debt Service 0 61 -61 0 61 -61 2.5 Other Capital 12538 14537 -1999 16568 20554 -3986 3 Errors & Omissions 795 0 795 0 823 -823 4 Monetary Movements (4.1+ 4.2) 0 5226 -5226 0 4508 -4508 4.1 I.M.F. 0 0 0 0 0 0 4.2 Foreign Exchange Reserves (Increase - / Decrease +) 5226 -5226 4508 -4508 Note: P: Preliminary. 162 RBI Bulletin November 2025CURRENT STATISTICS No. 40: India’s Overall Balance of Payments (₹ Crore) Apr-Jun 2024 Apr-Jun 2025 (P) Credit Debit Net Credit Debit Net Item 1 2 3 4 5 6 Overall Balance Of Payments (1+2+3) 4230634 4187037 43597 4669620 4631057 38563 1 Current Account (1.1+ 1.2) 2017438 2089818 -72379 2196416 2216691 -20275 1.1 Merchandise 927317 1459598 -532281 967474 1553197 -585723 1.2 Invisibles (1.2.1+1.2.2+1.2.3) 1090122 630220 459902 1228942 663494 565447 1.2.1 Services 738001 406971 331030 833507 423543 409964 1.2.1.1 Travel 61335 76511 -15177 50087 77720 -27633 1.2.1.2 Transportation 70959 71816 -856 65943 71818 -5876 1.2.1.3 Insurance 7534 4950 2584 7924 5245 2679 1.2.1.4 G.n.i.e. 1346 2575 -1229 1148 2761 -1612 1.2.1.5 Miscellaneous 596827 251118 345709 708405 265999 442406 1.2.1.5.1 Software Services 349760 37363 312397 404862 50070 354792 1.2.1.5.2 Business Services 191873 138694 53178 252469 135754 116715 1.2.1.5.3 Financial Services 18478 10572 7906 16636 6012 10624 1.2.1.5.4 Communication Services 4331 3702 629 4305 3251 1053 1.2.2 Transfers 246264 26824 219440 291287 25911 265375 1.2.2.1 Official 153 2599 -2446 172 1858 -1686 1.2.2.2 Private 246112 24225 221887 291114 24053 267061 1.2.3 Income 105857 196425 -90569 104148 214040 -109892 1.2.3.1 Investment Income 88028 188267 -100239 85926 205255 -119329 1.2.3.2 Compensation of Employees 17829 8158 9670 18222 8785 9437 2 Capital Account (2.1+2.2+2.3+2.4+2.5) 2206565 2097219 109346 2473204 2407326 65878 2.1 Foreign Investment (2.1.1+2.1.2) 1533057 1473256 59801 1484840 1422274 62567 2.1.1 Foreign Direct Investment 199586 147666 51920 232886 184078 48808 2.1.1.1 In India 190016 101538 88478 227624 106733 120892 2.1.1.1.1 Equity 136827 97382 39445 166121 103325 62795 2.1.1.1.2 Reinvested Earnings 43588 0 43588 50274 0 50274 2.1.1.1.3 Other Capital 9600 4156 5444 11230 3407 7822 2.1.1.2 Abroad 9570 46128 -36558 5262 77345 -72084 2.1.1.2.1 Equity 9570 22755 -13184 5262 40654 -35392 2.1.1.2.2 Reinvested Earnings 0 14278 -14278 0 16116 -16116 2.1.1.2.3 Other Capital 0 9095 -9095 0 20575 -20575 2.1.2 Portfolio Investment 1333471 1325590 7881 1251954 1238196 13759 2.1.2.1 In India 1328434 1320949 7485 1242216 1220994 21223 2.1.2.1.1 FIIs 1328434 1320949 7485 1242216 1220994 21223 2.1.2.1.1.1 Equity 1166461 1174878 -8416 1057724 1011603 46121 2.1.2.1.1.2 Debt 161973 146071 15901 184493 209391 -24898 2.1.2.1.2 ADR/GDRs 0 0 0 0 0 0 2.1.2.2 Abroad 5037 4641 396 9738 17202 -7464 2.2 Loans (2.2.1+2.2.2+2.2.3) 265411 222623 42788 558876 507639 51237 2.2.1 External Assistance 30365 18913 11451 26690 20513 6177 2.2.1.1 By India 52 217 -166 52 94 -42 2.2.1.2 To India 30313 18696 11617 26638 20419 6219 2.2.2 Commercial Borrowings 105337 92583 12753 399987 361074 38913 2.2.2.1 By India 34517 35497 -980 308187 300735 7452 2.2.2.2 To India 70820 57087 13733 91800 60339 31461 2.2.3 Short Term to India 129710 111126 18583 132199 126052 6147 2.2.3.1 Buyers' credit & Suppliers' Credit >180 days 114529 111126 3402 132199 117110 15089 2.2.3.2 Suppliers' Credit up to 180 days 15181 0 15181 0 8942 -8942 2.3 Banking Capital (2.3.1+2.3.2) 303498 279556 23942 287747 301047 -13300 2.3.1 Commercial Banks 302487 279556 22931 287664 301047 -13384 2.3.1.1 Assets 89303 113205 -23902 73395 111925 -38530 2.3.1.2 Liabilities 213184 166351 46833 214268 189122 25147 2.3.1.2.1 Non-Resident Deposits 195426 161851 33575 203422 172503 30920 2.3.2 Others 1011 0 1011 84 0 84 2.4 Rupee Debt Service 0 508 -508 0 524 -524 2.5 Other Capital 104599 121277 -16677 141740 175841 -34101 3 Errors & Omissions 6630 0 6630 0 7040 -7040 4 Monetary Movements (4.1+ 4.2) 0 43597 -43597 0 38563 -38563 4.1 I.M.F. 0 0 0 0 0 0 4.2 Foreign Exchange Reserves (Increase - / Decrease +) 0 43597 -43597 0 38563 -38563 Note: P: Preliminary. RBI Bulletin November 2025 163CURRENT STATISTICS No. 41: Standard Presentation of BoP in India as per BPM6 (US$ Million) Item Apr-Jun 2024 Apr-Jun 2025 (P) Credit Debit Net Credit Debit Net 1 2 3 4 5 6 1 Current Account (1.A+1.B+1.C) 241831 250477 -8646 256736 259087 -2351 1.A Goods and Services (1.A.a+1.A.b) 199623 223747 -24124 210514 231059 -20544 1.A.a Goods (1.A.a.1 to 1.A.a.3) 111158 174963 -63805 113087 181551 -68464 1.A.a.1 General merchandise on a BOP basis 111119 166616 -55497 112707 174065 -61359 1.A.a.2 Net exports of goods under merchanting 39 0 39 380 0 380 1.A.a.3 Nonmonetary gold 8347 -8347 7486 -7486 1.A.b Services (1.A.b.1 to 1.A.b.13) 88465 48784 39681 97428 49507 47920 1.A.b.1 Manufacturing services on physical inputs owned by others 268 22 246 253 40 213 1.A.b.2 Maintenance and repair services n.i.e. 81 238 -157 76 267 -191 1.A.b.3 Transport 8506 8609 -103 7708 8395 -687 1.A.b.4 Travel 7352 9171 -1819 5855 9085 -3230 1.A.b.5 Construction 1478 563 915 1098 891 207 1.A.b.6 Insurance and pension services 903 593 310 926 613 313 1.A.b.7 Financial services 2215 1267 948 1945 703 1242 1.A.b.8 Charges for the use of intellectual property n.i.e. 341 4448 -4107 446 5352 -4906 1.A.b.9 Telecommunications, computer, and information services 42541 5215 37326 47932 6470 41462 1.A.b.10 Other business services 23000 16625 6375 29511 15868 13643 1.A.b.11 Personal, cultural, and recreational services 1175 1249 -74 1210 1157 54 1.A.b.12 Government goods and services n.i.e. 161 309 -147 134 323 -188 1.A.b.13 Others n.i.e. 444 475 -31 334 345 -11 1.B Primary Income (1.B.1 to 1.B.3) 12689 23546 -10857 12174 25019 -12845 1.B.1 Compensation of employees 2137 978 1159 2130 1027 1103 1.B.2 Investment income 8660 21944 -13284 8498 23345 -14846 1.B.2.1 Direct investment 3384 12672 -9288 3156 14533 -11377 1.B.2.2 Portfolio investment 70 2411 -2341 113 1815 -1702 1.B.2.3 Other investment 1110 6641 -5531 1001 6826 -5825 1.B.2.4 Reserve assets 4095 220 3876 4228 170 4058 1.B.3 Other primary income 1892 624 1268 1545 647 898 1.C Secondary Income (1.C.1+1.C.2) 29520 3185 26335 34048 3010 31038 1.C.1 Financial corporations, nonfinancial corporations, households, and NPISHs 29502 2904 26598 34028 2812 31216 1.C.1.1 Personal transfers (Current transfers between resident and/non-resident households) 28644 1989 26655 33162 2061 31101 1.C.1.2 Other current transfers 857 914 -57 866 750 115 1.C.2 General government 18 281 -263 20 198 -178 2 Capital Account (2.1+2.2) 185 150 35 177 577 -400 2.1 Gross acquisitions (DR.)/disposals (CR.) of non-produced nonfinancial assets 4 45 -41 23 398 -374 2.2 Capital transfers 182 105 76 154 179 -26 3 Financial Account (3.1 to 3.5) 264317 256501 7816 288913 285339 3574 3.1 Direct Investment (3.1A+3.1B) 23925 17701 6224 27222 21517 5705 3.1.A Direct Investment in India 22777 12171 10606 26607 12476 14131 3.1.A.1 Equity and investment fund shares 21627 11673 9953 25294 12078 13217 3.1.A.1.1 Equity other than reinvestment of earnings 16402 11673 4728 19418 12078 7340 3.1.A.1.2 Reinvestment of earnings 5225 5225 5876 5876 3.1.A.2 Debt instruments 1151 498 653 1313 398 914 3.1.A.2.1 Direct investor in direct investment enterprises 1151 498 653 1313 398 914 3.1.B Direct Investment by India 1147 5529 -4382 615 9041 -8426 3.1.B.1 Equity and investment fund shares 1147 4439 -3292 615 6636 -6021 3.1.B.1.1 Equity other than reinvestment of earnings 1147 2728 -1580 615 4752 -4137 3.1.B.1.2 Reinvestment of earnings 1712 -1712 1884 -1884 3.1.B.2 Debt instruments 0 1090 -1090 0 2405 -2405 3.1.B.2.1 Direct investor in direct investment enterprises 1090 -1090 2405 -2405 3.2 Portfolio Investment 159844 158899 945 146339 144731 1608 3.2.A Portfolio Investment in India 159240 158343 897 145201 142720 2481 3.2.1 Equity and investment fund shares 139824 140833 -1009 123636 118245 5391 3.2.2 Debt securities 19416 17510 1906 21565 24475 -2910 3.2.B Portfolio Investment by India 604 556 48 1138 2011 -872 3.3 Financial derivatives (other than reserves) and employee stock options 6053 9666 -3613 5501 10588 -5087 3.4 Other investment 74496 65009 9487 109851 103996 5855 3.4.1 Other equity (ADRs/GDRs) 0 0 0 0 0 0 3.4.2 Currency and deposits 23547 19401 4146 23788 20164 3624 3.4.2.1 Central bank (Rupee Debt Movements; NRG) 121 0 121 10 0 10 3.4.2.2 Deposit-taking corporations, except the central bank (NRI Deposits) 23426 19401 4025 23778 20164 3614 3.4.2.3 General government 0 0 3.4.2.4 Other sectors 0 0 3.4.3 Loans (External Assistance, ECBs and Banking Capital) 29100 27475 1626 59721 59629 92 3.4.3.A Loans to India 24956 23193 1763 23691 24465 -774 3.4.3.B Loans by India 4144 4281 -137 36030 35163 866 3.4.4 Insurance, pension, and standardized guarantee schemes 47 133 -86 43 92 -49 3.4.5 Trade credit and advances 15548 13321 2228 15453 14734 719 3.4.6 Other accounts receivable/payable - other 6253 4680 1574 10848 9378 1470 3.4.7 Special drawing rights 0 0 3.5 Reserve assets 0 5226 -5226 0 4508 -4508 3.5.1 Monetary gold 0 0 3.5.2 Special drawing rights n.a. 0 0 3.5.3 Reserve position in the IMF n.a. 0 0 3.5.4 Other reserve assets (Foreign Currency Assets) 0 5226 -5226 0 4508 -4508 4 Total assets/liabilities 264317 256501 7816 288913 285339 3574 4.1 Equity and investment fund shares 169302 167301 2001 156227 149648 6579 4.2 Debt instruments 88762 79295 9467 121838 121805 33 4.3 Other financial assets and liabilities 6253 9906 -3652 10848 13886 -3038 5 Net errors and omissions 795 0 795 0 823 -823 Note: P: Preliminary. 164 RBI Bulletin November 2025CURRENT STATISTICS No. 42: Standard Presentation of BoP in India as per BPM6 (₹ Crore) Apr-Jun 2024 Apr-Jun 2025 (P) Item Credit Debit Net Credit Debit Net 1 2 3 4 5 6 1 Current Account (1.A+1.B+1.C) 2017436 2089564 -72128 2196412 2216528 -20117 1.A Goods and Services (1.A.a+1.A.b) 1665317 1866568 -201251 1800981 1976740 -175759 1.A.a Goods (1.A.a.1 to 1.A.a.3) 927317 1459598 -532281 967474 1553197 -585723 1.A.a.1 General merchandise on a BOP basis 926993 1389964 -462971 964221 1489154 -524933 1.A.a.2 Net exports of goods under merchanting 324 0 324 3253 0 3253 1.A.a.3 Nonmonetary gold 0 69634 -69634 0 64043 -64043 1.A.b Services (1.A.b.1 to 1.A.b.13) 738001 406970 331030 833507 423543 409964 1.A.b.1 Manufacturing services on physical inputs owned by others 2234 183 2051 2162 341 1821 1.A.b.2 Maintenance and repair services n.i.e. 676 1983 -1307 654 2286 -1632 1.A.b.3 Transport 70959 71816 -856 65943 71818 -5876 1.A.b.4 Travel 61335 76511 -15177 50087 77720 -27633 1.A.b.5 Construction 12327 4693 7635 9390 7619 1771 1.A.b.6 Insurance and pension services 7534 4950 2584 7924 5245 2679 1.A.b.7 Financial services 18478 10572 7906 16636 6012 10624 1.A.b.8 Charges for the use of intellectual property n.i.e. 2843 37103 -34261 3818 45787 -41969 1.A.b.9 Telecommunications, computer, and information services 354891 43507 311384 410065 55353 354712 1.A.b.10 Other business services 191873 138694 53178 252469 135754 116715 1.A.b.11 Personal, cultural, and recreational services 9803 10418 -615 10353 9894 459 1.A.b.12 Government goods and services n.i.e. 1346 2575 -1229 1148 2761 -1612 1.A.b.13 Others n.i.e. 3703 3965 -262 2858 2953 -95 1.B Primary Income (1.B.1 to 1.B.3) 105857 196425 -90569 104148 214040 -109892 1.B.1 Compensation of employees 17829 8158 9670 18222 8785 9437 1.B.2 Investment income 72241 183061 -110820 72705 199717 -127012 1.B.2.1 Direct investment 28232 105711 -77480 27000 124332 -97331 1.B.2.2 Portfolio investment 582 20112 -19530 969 15529 -14560 1.B.2.3 Other investment 9262 55405 -46143 8563 58401 -49838 1.B.2.4 Reserve assets 34166 1833 32333 36172 1455 34717 1.B.3 Other primary income 15787 5206 10581 13221 5537 7683 1.C Secondary Income (1.C.1+1.C.2) 246262 26570 219692 291283 25749 265534 1.C.1 Financial corporations, nonfinancial corporations, households, and NPISHs 246112 24225 221887 291114 24053 267061 1.C.1.1 Personal transfers (Current transfers between resident and/non-resident households) 238960 16597 222364 283709 17634 266075 1.C.1.2 Other current transfers 7151 7628 -477 7405 6419 986 1.C.2 General government 150 2345 -2195 168 1695 -1527 2 Capital Account (2.1+2.2) 1547 1253 295 1515 4936 -3421 2.1 Gross acquisitions (DR.)/disposals (CR.) of non-produced nonfinancial assets 32 375 -343 199 3401 -3202 2.2 Capital transfers 1515 878 637 1316 1535 -219 3 Financial Account (3.1 to 3.5) 2205020 2139817 65203 2471694 2441116 30578 3.1 Direct Investment (3.1A+3.1B) 199586 147666 51920 232886 184078 48808 3.1.A Direct Investment in India 190016 101538 88478 227624 106733 120892 3.1.A.1 Equity and investment fund shares 180416 97382 83034 216395 103325 113069 3.1.A.1.1 Equity other than reinvestment of earnings 136827 97382 39445 166121 103325 62795 3.1.A.1.2 Reinvestment of earnings 43588 0 43588 50274 0 50274 3.1.A.2 Debt instruments 9600 4156 5444 11230 3407 7822 3.1.A.2.1 Direct investor in direct investment enterprises 9600 4156 5444 11230 3407 7822 3.1.B Direct Investment by India 9570 46128 -36558 5262 77345 -72084 3.1.B.1 Equity and investment fund shares 9570 37033 -27463 5262 56770 -51509 3.1.B.1.1 Equity other than reinvestment of earnings 9570 22755 -13184 5262 40654 -35392 3.1.B.1.2 Reinvestment of earnings 0 14278 -14278 0 16116 -16116 3.1.B.2 Debt instruments 0 9095 -9095 0 20575 -20575 3.1.B.2.1 Direct investor in direct investment enterprises 0 9095 -9095 0 20575 -20575 3.2 Portfolio Investment 1333471 1325590 7881 1251954 1238196 13759 3.2.A Portfolio Investment in India 1328434 1320949 7485 1242216 1220994 21223 3.2.1 Equity and investment fund shares 1166461 1174878 -8416 1057724 1011603 46121 3.2.2 Debt securities 161973 146071 15901 184493 209391 -24898 3.2.B Portfolio Investment by India 5037 4641 396 9738 17202 -7464 3.3 Financial derivatives (other than reserves) and employee stock options 50493 80637 -30144 47060 90579 -43519 3.4 Other investment 621470 542327 79143 939793 889701 50093 3.4.1 Other equity (ADRs/GDRs) 0 0 0 0 0 0 3.4.2 Currency and deposits 196437 161851 34586 203506 172503 31003 3.4.2.1 Central bank (Rupee Debt Movements; NRG) 1011 0 1011 84 0 84 3.4.2.2 Deposit-taking corporations, except the central bank (NRI Deposits) 195426 161851 33575 203422 172503 30920 3.4.2.3 General government 0 0 0 0 0 0 3.4.2.4 Other sectors 0 0 0 0 0 0 3.4.3 Loans (External Assistance, ECBs and Banking Capital) 242762 229201 13561 510918 510131 787 3.4.3.A Loans to India 208194 193487 14707 202679 209303 -6624 3.4.3.B Loans by India 34569 35714 -1146 308239 300829 7410 3.4.4 Insurance, pension, and standardized guarantee schemes 396 1109 -714 366 783 -417 3.4.5 Trade credit and advances 129710 111126 18583 132199 126052 6147 3.4.6 Other accounts receivable/payable - other 52166 39039 13127 92803 80231 12572 3.4.7 Special drawing rights 0 0 0 0 0 0 3.5 Reserve assets 0 43597 -43597 0 38563 -38563 3.5.1 Monetary gold 0 0 0 0 0 0 3.5.2 Special drawing rights n.a. 0 0 0 0 0 0 3.5.3 Reserve position in the IMF n.a. 0 0 0 0 0 0 3.5.4 Other reserve assets (Foreign Currency Assets) 0 43597 -43597 0 38563 -38563 4 Total assets/liabilities 2205020 2139817 65203 2471694 2441116 30578 4.1 Equity and investment fund shares 1412373 1395681 16693 1336545 1280262 56282 4.2 Debt instruments 740481 661501 78980 1042346 1042060 286 4.3 Other financial assets and liabilities 52166 82636 -30470 92803 118794 -25990 5 Net errors and omissions 6630 0 6630 0 7040 -7040 Note: P: Preliminary. RBI Bulletin November 2025 165CURRENT STATISTICS No. 43: India’s International Investment Position (US$ Million) Item As on Financial Year/Quarter End 2024-25 2024 2025 Jun. Mar. Jun. Assets Liabilities Assets Liabilities Assets Liabilities Assets Liabilities 1 2 3 4 5 6 7 8 1. Direct investment Abroad/in India 270441 556903 246653 552829 270441 556903 278867 571227 1.1 Equity Capital* 173559 521931 156635 520605 173559 521931 179580 535378 1.2 Other Capital 96882 34972 90018 32224 96882 34972 99287 35849 2. Portfolio investment 15426 272042 12410 277347 15426 272042 16305 272544 2.1 Equity 10391 141938 10665 160898 10391 141938 13111 147392 2.2 Debt 5034 130104 1745 116449 5034 130104 3193 125152 3. Other investment 186700 641155 140909 588623 186700 641155 195426 657723 3.1 Trade credit 33422 131164 32822 125907 33422 131164 33782 131887 3.2 Loan 25891 250109 20803 224491 25891 250109 24464 259789 3.3 Currency and Deposits 79332 167598 57747 160628 79332 167598 82528 171749 3.4 Other Assets/Liabilities 48055 92285 29537 77597 48055 92285 54651 94298 4. Reserves 668326 651997 668326 698118 5. Total Assets/ Liabilities 1140893 1470099 1051969 1418799 1140893 1470099 1188715 1501494 6. Net IIP (Assets - Liabilities) -329206 -366830 -329206 -312779 Note: * Equity capital includes share of investment funds and reinvested earnings. 166 RBI Bulletin November 2025CURRENT STATISTICS Payment and Settlement Systems No. 44: Payment System Indicators PART I - Payment System Indicators - Payment & Settlement System Statistics System Volume (Lakh) Value (₹ Crore) FY 2024-25 2024 2025 FY 2024-25 2024 2025 Sep. Aug. Sep. Sep. Aug. Sep. 1 2 3 4 5 6 7 8 A. Settlement Systems Financial Market Infrastructures (FMIs) 1 CCIL Operated Systems (1.1 to 1.3) 47.40 4.08 4.28 4.95 296218030 23840258 28151946 30270023 1.1 Govt. Securities Clearing (1.1.1 to 1.1.3) 17.87 1.57 1.50 1.75 185733719 14750412 16614170 18003263 1.1.1 Outright 10.56 0.99 0.81 1.04 16056018 1468346 1240951 1499643 1.1.2 Repo 4.72 0.37 0.47 0.48 77286611 6038102 7489700 7926471 1.1.3 Tri-party Repo 2.58 0.21 0.22 0.23 92391091 7243965 7883520 8577149 1.2 Forex Clearing 28.06 2.40 2.69 3.11 100639565 8415273 10788097 11417001 1.3 Rupee Derivatives @ 1.46 0.11 0.10 0.09 9844746 674573 749679 849759 B. Payment Systems I Financial Market Infrastructures (FMIs) - - - - - - - - 1 Credit Transfers - RTGS (1.1 to 1.2) 3024.55 233.33 259.68 280.83 201387682 17786483 16371216 19836942 1.1 Customer Transactions 3010.32 232.21 258.52 279.64 181153129 16027655 14993007 18220475 1.2 Interbank Transactions 14.23 1.12 1.16 1.19 20234553 1758828 1378209 1616467 II Retail 2 Credit Transfers - Retail (2.1 to 2.6) 2061014.91 166626.30 218816.17 213074.92 79781976 6384754 7102303 7543380 2.1 AePS (Fund Transfers) @ 3.64 0.30 0.31 0.26 190 14 16 12 2.2 APBS $ 32964.43 2342.41 3817.08 2542.90 554034 35550 66150 41746 2.3 IMPS 56249.68 4299.36 4772.62 3943.79 7139110 565233 597549 596847 2.4 NACH Cr $ 16938.86 1657.93 1854.46 1850.43 1670223 122079 167856 149729 2.5 NEFT 96198.05 7908.83 8288.60 8403.21 44361464 3597885 3785260 4265310 2.6 UPI @ 1858660.25 150417.47 200083.10 196334.33 26056955 2063995 2485473 2489737 2.6.1 of which USSD @ 17.24 1.31 0.69 0.94 185 14 7 10 3 Debit Transfers and Direct Debits (3.1 to 3.3) 21659.95 1805.36 1929.02 1926.97 2208583 180707 214928 222919 3.1 BHIM Aadhaar Pay @ 230.08 18.81 25.38 18.90 6907 568 718 591 3.2 NACH Dr $ 19762.28 1651.69 1764.13 1786.47 2199327 179945 214031 222164 3.3 NETC (linked to bank account) @ 1667.59 134.86 139.51 121.60 2349 193 178 164 4 Card Payments (4.1 to 4.2) 63861.15 5275.26 6056.57 5999.81 2605110 216304 228576 253483 4.1 Credit Cards (4.1.1 to 4.1.2) 47740.76 3921.62 4935.77 4952.41 2109197 176202 191170 216707 4.1.1 PoS based $ 24571.10 1978.05 2501.63 2416.15 795022 60857 72753 72544 4.1.2 Others $ 23169.66 1943.57 2434.15 2536.26 1314175 115345 118417 144163 4.2 Debit Cards (4.2.1 to 4.2.1 ) 16120.39 1353.64 1120.80 1047.40 495914 40102 37406 36776 4.2.1 PoS based $ 11980.33 995.05 843.59 777.57 332556 25777 24663 22773 4.2.2 Others $ 4140.06 358.59 277.21 269.83 163358 14325 12743 14003 5 Prepaid Payment Instruments (5.1 to 5.2) 70254.08 5476.69 7966.16 8551.51 216751 17489 22253 22637 5.1 Wallets 52898.40 4054.95 6246.19 6871.58 154066 11889 17169 17183 5.2 Cards (5.2.1 to 5.2.2) 17355.68 1421.74 1719.97 1679.93 62686 5600 5083 5454 5.2.1 PoS based $ 8240.14 721.65 669.51 688.72 11512 858 1030 1092 5.2.2 Others $ 9115.54 700.09 1050.45 991.22 51174 4743 4054 4362 6 Paper-based Instruments (6.1 to 6.2) 6095.38 484.94 446.03 464.86 7113350 543387 540025 570467 6.1 CTS (NPCI Managed) 6095.38 484.94 446.03 464.86 7113350 543387 540025 570467 6.2 Others 0.00 – – – – – – – Total - Retail Payments (2+3+4+5+6) 2222885.46 179668.57 235213.95 230018.07 91925771 7342641 8108084 8612885 Total Payments (1+2+3+4+5+6) 2225910.01 179901.90 235473.63 230298.90 293313453 25129124 24479300 28449827 Total Digital Payments (1+2+3+4+5) 2219814.63 179416.96 235027.60 229834.04 286200103 24585737 23939275 27879361 RBI Bulletin November 2025 167CURRENT STATISTICS PART II - Payment Modes and Channels System Volume (Lakh) Value (₹ Crore) FY 2024-25 2024 2025 FY 2024-25 2024 2025 Sep. Aug. Sep. Sep. Aug. Sep. 1 2 3 4 5 6 7 8 A. Other Payment Channels 1 Mobile Payments (mobile app based) (1.1 to 1.2) 1756976.91 144028.12 184421.97 181008.61 39206221 3157064 3539156 3618114 1.1 Intra-bank $ 110801.96 10596.33 10513.94 10469.49 7207439 617325 605043 633733 1.2 Inter-bank $ 1646174.95 133431.79 173908.03 170539.12 31998782 2539739 2934113 2984381 2 Internet Payments (Netbanking / Internet Browser Based) @ (2.1 to 2.2) 47478.09 3911.28 3638.12 3755.37 131858133 11251665 11554723 13794692 2.1 Intra-bank @ 13056.37 1103.93 798.11 853.80 69086996 5946842 6020479 7091903 2.2 Inter-bank @ 34421.72 2807.35 2840.01 2901.57 62771136 5304823 5534243 6702789 B. ATMs 3 Cash Withdrawal at ATMs $ (3.1 to 3.3) 60308.11 4949.81 4625.94 4395.46 3063077 245223 240098 230952 3.1 Using Credit Cards $ 97.25 8.02 6.71 6.69 5084 417 366 369 3.2 Using Debit Cards $ 59965.70 4922.18 4601.37 4370.89 3046987 243930 238883 229713 3.3 Using Pre-paid Cards $ 245.16 19.62 17.86 17.89 11005 876 849 870 4 Cash Withdrawal at PoS $ (4.1 to 4.2) 3.58 0.27 0.13 0.12 37 3 1 2 4.1 Using Debit Cards $ 3.33 0.25 0.10 0.10 35 3 1 1 4.2 Using Pre-paid Cards $ 0.25 0.01 0.02 0.02 3 0 0 0 5 Cash Withrawal at Micro ATMs @ 11640.55 975.12 1245.48 1034.96 296622 23389 31157 26356 5.1 AePS @ 11640.55 975.12 1245.48 1034.96 296622 23389 31157 26356 PART III - Payment Infrastructures (Lakh) System As on March 2024 2025 2025 Sep. Aug. Sep. 1 2 3 4 Payment System Infrastructures 1 Number of Cards (1.1 to 1.2) 11006.97 10793.85 11303.57 11382.14 1.1 Credit Cards 1098.85 1061.03 1123.14 1133.90 1.2 Debit Cards 9908.12 9732.82 10180.42 10248.24 2 Number of PPIs @ (2.1 to 2.2) 13401.46 15339.99 14584.13 16180.40 2.1 Wallets @ 8678.44 11381.79 9692.18 11478.95 2.2 Cards @ 4723.02 3958.20 4891.95 4701.45 3 Number of ATMs (3.1 to 3.2) 2.56 2.55 2.49 2.49 3.1 Bank owned ATMs $ 2.20 2.20 2.12 2.12 3.2 White Label ATMs $ 0.36 0.35 0.36 0.37 4 Number of Micro ATMs @ 14.82 14.53 14.71 14.60 5 Number of PoS Terminals 110.98 93.43 119.73 121.24 6 Bharat QR @ 67.18 64.16 65.97 60.95 7 UPI QR * 6579.30 6070.79 6978.38 7090.66 @: New inclusion w.e.f. November 2019 #: Data reported by Co-operative Banks, LABs and RRBs included with effect from December 2021. $ : Inclusion separately initiated from November 2019 - would have been part of other items hitherto. *: New inclusion w.e.f. September 2020; Includes only static UPI QR Code Note : 1. Data is provisional. 2. ECS (Debit and Credit) has been merged with NACH with effect from January 31, 2020. 3. The data from November 2019 onwards for card payments (Debit/Credit cards) and Prepaid Payment Instruments (PPIs) may not be comparable with earlier months/ periods, as more granular data is being published along with revision in data definitions. 4. Only domestic financial transactions are considered. The new format captures e-commerce transactions; transactions using FASTags, digital bill payments and card-to-card transfer through ATMs, etc.. Also, failed transactions, chargebacks, reversals, expired cards/ wallets, are excluded. Part I-A. Settlement systems 1.1.3: Tri- party Repo under the securities segment has been operationalised from November 05, 2018. Part I-B. Payments systems 4.1.2: ‘Others’ includes e-commerce transactions and digital bill payments through ATMs, etc. 4.2.2: ‘Others’ includes e-commerce transactions, card to card transfers and digital bill payments through ATMs, etc. 5: Available from December 2010. 5.1: includes purchase of goods and services and fund transfer through wallets. 5.2.2: includes usage of PPI Cards for online transactions and other transactions. 6.1: Pertain to three grids – Mumbai, New Delhi and Chennai. 6.2: ‘Others’ comprises of Non-MICR transactions which pertains to clearing houses managed by 21 banks. Part II-A. Other payment channels 1: Mobile Payments – o Include transactions done through mobile apps of banks and UPI apps. o The data from July 2017 includes only individual payments and corporate payments initiated, processed, and authorised using mobile device. Other corporate payments which are not initiated, processed, and authorised using mobile device are excluded. 2: Internet Payments – includes only e-commerce transactions through ‘netbanking’ and any financial transaction using internet banking website of the bank. Part II-B. ATMs 3.3 and 4.2: only relates to transactions using bank issued PPIs. Part III. Payment systems infrastructure 3: Includes ATMs deployed by Scheduled Commercial Banks (SCBs) and White Label ATM Operators (WLAOs). WLAs are included from April 2014 onwards. 168 RBI Bulletin November 2025CURRENT STATISTICS Occasional Series No. 45: Small Savings (₹ Crore) Scheme 2023-24 2024 2025 Feb. Dec. Jan. Feb. 1 2 3 4 5 1 Small Savings Receipts 232460 14570 11133 12581 11379 Outstanding 1865029 1819758 1982465 1994553 2005585 1.1 Total Deposits Receipts 161344 10025 8734 9178 8077 Outstanding 1298795 1268920 1395484 1404661 1412738 1.1.1 Post Office Saving Bank Deposits Receipts 17229 1520 1090 2702 814 Outstanding 191692 218498 201999 204701 205515 1.1.2 Sukanya Samriddhi Yojna Receipts 35174 2233 2244 2347 2282 Outstanding 157611 109222 177007 179354 181636 1.1.3 National Saving Scheme, 1987 Receipts 0 0 0 0 0 Outstanding 0 0 0 0 0 1.1.4 National Saving Scheme, 1992 Receipts 0 0 0 0 0 Outstanding 0 0 0 0 0 1.1.5 Monthly Income Scheme Receipts 26696 1927 827 1279 1045 Outstanding 269007 267205 282142 283421 284466 1.1.6 Senior Citizen Scheme 2004 Receipts 38167 2153 1531 1922 1952 Outstanding 175472 173476 194605 196527 198479 1.1.7 Post Office Time Deposits Receipts 25341 2632 2125 2853 2108 Outstanding 305776 303000 330912 333764 335872 1.1.7.1 1 year Time Deposits Outstanding 140423 138552 159174 161578 163358 1.1.7.2 2 year Time Deposits Outstanding 11967 11730 14299 14476 14637 1.1.7.3 3 year Time Deposits Outstanding 8932 8782 10308 10487 10645 1.1.7.4 5 year Time Deposits Outstanding 144454 143936 147131 147223 147232 1.1.8 Post Office Recurring Deposits Receipts 18713 -420 1025 -1831 -25 Outstanding 197134 195727 207269 205438 205413 1.1.9 Post Office Cumulative Time Deposits Receipts 0 0 0 0 0 Outstanding 0 0 0 0 0 1.1.10 Other Deposits Receipts 8 -20 -108 -95 -100 Outstanding 1754 1444 1195 1100 1000 1.1.11 PM Care for children Receipts 16 0 0 1 1 Outstanding 349 348 355 356 357 1.2 Saving Certificates Receipts 56069 3940 2226 3019 2858 Outstanding 418021 414597 438074 440601 443112 1.2.1 National Savings Certificate VIII issue Receipts 16853 1446 430 796 762 Outstanding 183905 180181 192621 193417 194179 1.2.2 Indira Vikas Patras Receipts 0 0 0 0 0 Outstanding 0 0 0 0 0 1.2.3 Kisan Vikas Patras Receipts 0 0 0 0 0 Outstanding 0 0 0 0 0 1.2.4 Kisan Vikas Patras - 2014 Receipts 20939 1428 1113 1376 1247 Outstanding 220560 219498 228707 230083 231330 1.2.5 National Saving Certificate VI issue Receipts 0 0 0 0 0 Outstanding 0 0 0 0 0 1.2.6 National Saving Certificate VII issue Receipts 0 0 0 0 0 Outstanding 0 0 0 0 0 1.2.7 M.S. Certificates Receipts 18277 1066 683 847 849 Outstanding 18277 17235 25303 26150 26999 1.2.8 Other Certificates Outstanding -4721 -2317 -8557 -9049 -9396 1.3 Public Provident Fund Receipts 15047 605 173 384 444 Outstanding 148213 136241 148907 149291 149735 Note : Data on receipts from April 2017 are net receipts, i.e., gross receipt minus gross payment. Source: Accountant General, Post and Telegraphs. RBI Bulletin November 2025 169CURRENT STATISTICS No. 46 : Ownership Pattern of Central and State Governments Securities (Per cent) Central Government Dated Securities 2024 2025 Category Jun. Sep. Dec. Mar. Jun. 1 2 3 4 5 (A) Total (in ₹. Crore) 10946860 11271589 11422728 11642652 11854200 1 Commercial Banks 37.52 37.55 37.98 36.18 35.28 2 Co-operative Banks 1.42 1.35 1.36 1.29 1.29 3 Non-Bank PDs 0.70 0.77 0.65 0.76 0.59 4 Insurance Companies 26.11 25.95 26.14 25.81 25.95 5 Mutual Funds 2.87 3.14 3.11 2.68 2.46 6 Provident Funds 4.41 4.25 4.25 4.24 4.35 7 Pension Funds 4.74 4.86 5.05 4.91 4.96 8 Financial Institutions 0.57 0.63 0.64 0.71 0.74 9 Corporates 1.44 1.60 1.45 1.49 1.26 10 Foreign Portfolio Investors 2.34 2.80 2.81 3.12 2.80 11 RBI 11.92 11.16 10.55 12.78 14.21 12 Others 5.97 5.92 6.01 6.01 6.13 12.1 State Governments 2.13 2.19 2.21 2.25 2.29 State Governments Securities 2024 2025 Category Jun. Sep. Dec. Mar. Jun. 1 2 3 4 5 (B) Total (in ₹. Crore) 5727482 5909490 6055711 6399564 6524417 1 Commercial Banks 33.85 34.39 35.11 35.40 35.54 2 Co-operative Banks 3.38 3.29 3.22 3.08 3.02 3 Non-Bank PDs 0.59 0.60 0.53 0.61 0.60 4 Insurance Companies 25.85 25.56 25.16 24.07 24.12 5 Mutual Funds 2.08 1.93 1.89 1.93 1.84 6 Provident Funds 22.94 23.02 22.90 23.60 23.72 7 Pension Funds 4.87 4.87 4.82 5.07 4.96 8 Financial Institutions 1.58 1.57 1.58 1.48 1.59 9 Corporates 2.03 1.95 1.97 2.05 1.93 10 Foreign Portfolio Investors 0.05 0.04 0.03 0.05 0.02 11 RBI 0.62 0.60 0.58 0.55 0.54 12 Others 2.17 2.18 2.19 2.10 2.12 12.1 State Governments 0.26 0.26 0.26 0.25 0.25 Treasury Bills 2024 2025 Category Jun. Sep. Dec. Mar. Jun. 1 2 3 4 5 (C) Total (in ₹. Crore) 858193 747242 760045 790381 784059 1 Commercial Banks 47.79 44.74 40.45 46.58 42.87 2 Co-operative Banks 1.49 1.58 1.22 2.17 1.80 3 Non-Bank PDs 2.69 2.28 1.41 2.09 1.10 4 Insurance Companies 5.78 5.26 4.73 4.23 4.07 5 Mutual Funds 14.50 15.06 15.41 16.15 15.72 6 Provident Funds 0.60 0.26 0.04 0.20 0.09 7 Pension Funds 0.00 0.00 0.00 0.02 0.00 8 Financial Institutions 6.56 6.36 6.77 7.73 6.31 9 Corporates 4.79 4.66 4.56 4.50 3.77 10 Foreign Portfolio Investors 0.20 0.15 0.12 0.09 0.02 11 RBI 0.00 0.00 0.00 0.00 0.00 12 Others 15.59 19.65 25.29 16.23 24.26 12.1 State Governments 11.55 14.95 20.11 11.23 18.34 Notes: (1) The table format is revised since monthly Bulletin for the month of June 2023. (2) Central Government Dated Securities include special securities and Sovereign Gold Bonds. (3) State Government Securities include special bonds issued under Ujwal DISCOM Assurance Yojana (UDAY). (4) Bank PDs are clubbed under Commercial Banks. (5) The category ‘Others’ comprises State Governments, DICGC, PSUs, Trusts, Foreign Central Banks, HUF/ Individuals etc. (6) Data since September 2023 includes the impact of the merger of a non-bank with a bank. 170 RBI Bulletin November 2025CURRENT STATISTICS No. 47: Combined Receipts and Disbursements of the Central and State Governments (₹ Crore) Item 2019-20 2020-21 2021-22 2022-23 2023-24 RE 2024-25 BE 1 2 3 4 5 6 1 Total Disbursements 5410887 6353359 7098451 7880522 9110725 9800798 1.1 Developmental 3074492 3823423 4189146 4701611 5514584 5862996 1.1.1 Revenue 2446605 3150221 3255207 3574503 3965270 4195108 1.1.2 Capital 588233 550358 861777 1042159 1453849 1526993 1.1.3 Loans 39654 122844 72163 84949 95464 140895 1.2 Non-Developmental 2253027 2442941 2810388 3069896 3467270 3800321 1.2.1 Revenue 2109629 2271637 2602750 2895864 3266628 3537378 1.2.1.1 Interest Payments 955801 1060602 1226672 1377807 1562660 1711972 1.2.2 Capital 141457 169155 175519 171131 196073 259346 1.2.3 Loans 1941 2148 32119 2902 4569 3597 1.3 Others 83368 86995 98916 109015 128871 137481 2 Total Receipts 5734166 6397162 7156342 7855370 9054999 9650488 2.1 Revenue Receipts 3851563 3688030 4823821 5447913 6379349 7209647 2.1.1 Tax Receipts 3231582 3193390 4160414 4809044 5456913 6142276 2.1.1.1 Taxes on commodities and services 2012578 2076013 2626553 2865550 3248450 3631569 2.1.1.2 Taxes on Income and Property 1216203 1114805 1530636 1939550 2204462 2506181 2.1.1.3 Taxes of Union Territories (Without Legislature) 2800 2572 3225 3943 4001 4526 2.1.2 Non-Tax Receipts 619981 494640 663407 638870 922436 1067371 2.1.2.1 Interest Receipts 31137 33448 35250 42975 49552 57273 2.2 Non-debt Capital Receipts 110094 64994 44077 62716 86733 118239 2.2.1 Recovery of Loans & Advances 59515 16951 27665 15970 55895 45125 2.2.2 Disinvestment proceeds 50578 48044 16412 46746 30839 73114 3 Gross Fiscal Deficit [ 1 - ( 2.1 + 2.2 ) ] 1449230 2600335 2230553 2369892 2644642 2472912 3A Sources of Financing: Institution-wise 3A.1 Domestic Financing 1440548 2530155 2194406 2332768 2619811 2456959 3A.1.1 Net Bank Credit to Government 571872 890012 627255 687904 346483 ... 3A.1.1.1 Net RBI Credit to Government 190241 107493 350911 529 -257913 ... 3A.1.2 Non-Bank Credit to Government 868676 1640143 1567151 1644864 2273328 ... 3A.2 External Financing 8682 70180 36147 37124 24832 15952 3B Sources of Financing: Instrument-wise 3B.1 Domestic Financing 1440548 2530155 2194406 2332768 2619811 2456959 3B.1.1 Market Borrowings (net) 971378 1696012 1213169 1651076 1962969 1983757 3B.1.2 Small Savings (net) 209232 458801 526693 358764 434151 447511 3B.1.3 State Provident Funds (net) 38280 41273 28100 13880 21386 19857 3B.1.4 Reserve Funds 10411 4545 42153 68803 52385 -33653 3B.1.5 Deposits and Advances -14227 25682 42203 51989 35819 -10138 3B.1.6 Cash Balances -323279 -43802 -57891 25152 55726 150310 3B.1.7 Others 548753 347643 399980 163104 57374 -100684 3B.2 External Financing 8682 70180 36147 37124 24832 15952 4 Total Disbursements as per cent of GDP 26.9 32.0 30.1 29.2 30.8 30.0 5 Total Receipts as per cent of GDP 28.5 32.2 30.3 29.1 30.7 29.6 6 Revenue Receipts as per cent of GDP 19.2 18.6 20.4 20.2 21.6 22.1 7 Tax Receipts as per cent of GDP 16.1 16.1 17.6 17.8 18.5 18.8 8 Gross Fiscal Deficit as per cent of GDP 7.2 13.1 9.5 8.8 9.0 7.6 … : Not available; RE: Revised Estimates; BE: Budget Estimates Source : Budget Documents of Central and State Governments. Notes: GDP data is based on 2011-12 base. GDP for 2024-25 is from Union Budget 2024-25. Data pertains to all States and Union Territories. 1 & 2: Data are net of repayments of the Central Government (including repayments to the NSSF) and State Governments. 1.3: Represents compensation and assignments by States to local bodies and Panchayati Raj institutions. 2: Data are net of variation in cash balances of the Central and State Governments and includes borrowing receipts of the Central and State Governments. 3A.1.1: Data as per RBI records. 3B.1.1: Borrowings through dated securities. 3B.1.2: Represent net investment in Central and State Governments’ special securities by the National Small Savings Fund (NSSF). This data may vary from previous publications due to adjustments across components with availability of new data. 3B.1.6: Include Ways and Means Advances by the Centre to the State Governments. 3B.1.7: Include Treasury Bills, loans from financial institutions, insurance and pension funds, remittances, cash balance investment account. RBI Bulletin November 2025 171CURRENT STATISTICS No. 48: Financial Accommodation Availed by State Governments under various Facilities (₹ Crore) During September-2025 Sr. State/Union Territory Special Drawing Ways and Means Overdraft (OD) No Facility (SDF) Advances (WMA) Average Number Average Number Average Number amount of days amount of days amount of days availed availed availed availed availed availed 1 2 3 4 5 6 7 1 Andhra Pradesh 6822.75 30 2425.26 30 2635.60 16 2 Arunachal Pradesh - - - - - - 3 Assam 1307.57 21 - - - - 4 Bihar - - - - - - 5 Chhattisgarh 167.58 6 - - - - 6 Goa 70.72 1 - - - - 7 Gujarat - - - - - - 8 Haryana 785.63 18 68.36 1 - - 9 Himachal Pradesh - - 408.43 20 327.15 8 10 Jammu & Kashmir UT 37.45 6 183.51 6 - - 11 Jharkhand 1487.32 23 907.55 16 607.04 9 12 Karnataka - - - - - - 13 Kerala 1484.14 30 852.37 19 1944.39 1 14 Madhya Pradesh - - - - - - 15 Maharashtra - - - - - - 16 Manipur 67.86 7 128.91 2 - - 17 Meghalaya 341.10 29 146.10 4 - - 18 Mizoram 89.55 13 - - - - 19 Nagaland 452.73 30 - - - - 20 Odisha - - - - - - 21 Puducherry - - - - - - 22 Punjab 4886.42 30 1317.89 30 874.89 8 23 Rajasthan 3637.22 30 1309.15 20 - - 24 Tamil Nadu - - - - - - 25 Telangana 5028.59 30 1702.10 26 722.39 6 26 Tripura - - - - - - 27 Uttar Pradesh - - - - - - 28 Uttarakhand 981.29 30 - - - - 29 West Bengal - - - - - - Notes: 1. SDF is availed by State Governments against the collateral of Consolidated Sinking Fund (CSF), Guarantee Redemption Fund (GRF) & Auction Treasury Bills (ATBs) balances and other investments in government securities. 2. WMA is advance by Reserve Bank of India to State Governments for meeting temporary cash mismatches. 3. OD is advanced to State Governments beyond their WMA limits. 4. Average amount availed is the total accommodation (SDF/WMA/OD) availed divided by number of days for which accommodation was extended during the month. 5.- : Nil. Source: Reserve Bank of India. 172 RBI Bulletin November 2025CURRENT STATISTICS No. 49: Investments by State Governments (₹ Crore) As on end of September 2025 Consolidated Guarantee Sr. State/Union Government Auction Treasury Sinking Fund Redemption Fund No Territory Securities Bills (ATBs) (CSF) (GRF) 1 2 3 4 5 1 Andhra Pradesh 12176 1198 0 0 2 Arunachal Pradesh 3095 8 0 6350 3 Assam 8053 94 0 0 4 Bihar 15029 972 0 13500 5 Chhattisgarh 8644 1002 0 11630 6 Goa 1181 481 0 0 7 Gujarat 16048 701 0 2000 8 Haryana 2741 1796 0 0 9 Himachal Pradesh - - 0 0 10 Jammu & Kashmir UT 55 55 0 0 11 Jharkhand 3134 - 0 780 12 Karnataka 21288 786 0 56497 13 Kerala 3390 0 0 0 14 Madhya Pradesh - 1340 0 850 15 Maharashtra 73695 3221 0 0 16 Manipur 73 147 0 0 17 Meghalaya 1337 114 0 0 18 Mizoram 530 84 0 0 19 Nagaland 1994 49 0 0 20 Odisha 19166 2151 0 15955 21 Puducherry 609 - 0 1750 22 Punjab 10522 960 0 0 23 Rajasthan 2932 377 0 5750 24 Tamil Nadu 3620 - 0 4718 25 Telangana 8310 1823 0 0 26 Tripura 1385 31 0 0 27 Uttarakhand 5940 320 0 0 28 Uttar Pradesh 19690 5459 0 10000 29 West Bengal 15032 1137 0 10000 Total 259668 24309 0 139780 Notes: 1. CSF and GRF are reserve funds maintained by some State Governments with the Reserve Bank of India. 2. ATBs include investment by State Governments in Treasury bills of 91 days, 182 days and 364 days in the primary market. 3. - : Not Applicable (not a member of the scheme). RBI Bulletin November 2025 173CURRENT STATISTICS No. 50: Market Borrowings of State Governments (₹ Crore) 2025-26 Total amount 2023-24 2024-25 raised, so far in July August September 2025-26 Sr. No. State Gross Net Gross Net Gross Net Gross Net Gross Net Amount Amount Amount Amount Amount Amount Amount Amount Amount Amount Gross Net Raised Raised Raised Raised Raised Raised Raised Raised Raised Raised 1 2 3 4 5 6 7 8 9 10 11 12 13 1 Andhra Pradesh 68400 55330 78205 57123 5600 3300 5000 3800 5000 4000 42172 33172 2 Arunachal Pradesh 902 672 1010 704 - - - - - - - -130 3 Assam 18500 16000 19000 13850 1400 1400 1104 1104 2300 1800 8304 6854 4 Bihar 47612 29910 47546 30890 6000 6000 6000 6000 14000 11922 26000 23922 5 Chhattisgarh 32000 26213 24500 16913 - -700 - - 500 500 4470 3770 6 Goa 2550 1560 1050 250 100 - 300 200 200 - 800 100 7 Gujarat 30500 11947 38200 16280 3000 3000 3500 2500 3000 700 19500 8440 8 Haryana 47500 28364 49500 31710 3000 945 3000 2000 3500 1500 19500 9970 9 Himachal Pradesh 8072 5856 7359 4725 1919 1919 1500 1000 - -200 6419 5069 10 Jammu & Kashmir UT 16337 13904 13170 11416 1100 600 1100 650 700 700 5405 3955 11 Jharkhand 1000 -2505 3500 -2005 - -1000 - - 2000 2000 2000 1000 12 Karnataka 81000 63003 92025 71525 - - - - - - - -1000 13 Kerala 42438 26638 53666 37966 5000 2500 4988 1988 5000 5000 26988 16988 14 Madhya Pradesh 38500 26264 63400 47206 6800 5300 8800 7300 7000 5000 30877 24877 15 Maharashtra 110000 79738 123000 90917 24000 21000 12000 9000 8500 5500 66000 52000 16 Manipur 1426 1076 1500 1037 250 100 - - 350 350 1350 1000 17 Meghalaya 1364 912 1882 997 - -50 300 - 500 500 1650 1130 18 Mizoram 901 641 1169 939 100 100 100 100 150 90 475 340 19 Nagaland 2551 2016 1550 950 - - - - 400 250 400 50 20 Odisha 0 -4658 20780 17780 3000 3000 2000 2000 1000 1000 6000 6000 21 Puducherry 1100 475 1600 880 - -200 - - 350 350 550 350 22 Punjab 42386 29517 40828 32466 5000 4400 1500 - 2933 1521 25233 17579 23 Rajasthan 73624 49718 75185 49479 5500 4000 6000 5000 3000 500 38100 24538 24 Sikkim 1916 1701 1951 1621 - - - - 500 500 500 500 25 Tamil Nadu 113001 75970 123625 89894 7000 5500 8000 5600 9000 7500 48300 30650 26 Telangana 49618 39385 56209 42199 8500 6000 8000 7200 12000 10800 45900 35752 27 Tripura 0 -550 0 -150 - -200 - - - - 800 600 28 Uttar Pradesh 97650 85335 45000 23185 3000 1000 3000 2000 - -2000 12000 -2233 29 Uttarakhand 6300 3800 10400 8000 1000 1000 - -500 - -500 3000 1250 30 West Bengal 69910 48910 76500 54600 5500 4000 5500 4000 5500 4000 24000 15500 Grand Total 1007058 717140 1073310 753345 96769 72914 81692 60942 87383 63283 466692 321992 - : Nil. Note: The State of J&K has ceased to exist constitutionally from October 31, 2019 and the liabilities of the State continue to remain as liabilities of the new UT of Jammu and Kashmir. Source: Reserve Bank of India. 174 RBI Bulletin November 2025CURRENT STATISTICS No. 51 (a): Flow of Financial Assets and Liabilities of Households - Instrument-wise (Amount in ` Crore) 2022-23 Item Q1 Q2 Q3 Q4 Annual Net Financial Assets (I-II) 287802.7 297217.6 293954.9 451660.3 1330635.4 Per cent of GDP 4.4 4.6 4.3 6.4 4.9 I. Financial Assets 577822.4 632335.6 748109.7 968986.1 2927253.7 Per cent of GDP 8.9 9.8 11.0 13.6 10.9 of which: 1.Total Deposits (a+b) 185429.1 317361.2 280233.1 325852.7 1108876.2 (a) Bank Deposits 163172.4 299532.7 256399.7 307866.8 1026971.5 i. Commercial Banks 158613.3 300565.0 248459.8 284968.0 992606.2 ii. Co-operative Banks 4559.0 -1032.4 7939.8 22898.9 34365.3 (b) Non-Bank Deposits 22256.8 17828.6 23833.5 17985.9 81904.7 of which: Other Financial Institutions (i+ii) 6504.8 2076.7 8081.6 2234.0 18897.1 i. Non-Banking Financial Companies 4230.6 3267.2 3246.9 3945.8 14690.4 ii. Housing Finance Companies 2274.2 -1190.5 4834.7 -1711.8 4206.6 2. Life Insurance Funds 73357.5 151737.1 167581.7 156268.5 548944.9 3. Provident and Pension Funds (including PPF) 146719.1 118171.9 136388.4 216513.6 617793.1 4. Currency 66438.9 -54579.3 76760.1 148990.1 237609.7 5. Investments 51502.6 48530.1 49778.6 64150.6 213961.9 of which: (a) Mutual Funds 35443.5 44484.0 40205.9 58954.5 179087.8 (b) Equity 13560.9 1378.2 6434.1 1664.9 23038.1 6. Small Savings (excluding PPF) 54375.1 51114.5 37367.7 57210.6 200068.0 II. Financial Liabilities 290019.7 335118.0 454154.8 517325.8 1596618.3 Per cent of GDP 4.5 5.2 6.7 7.3 5.9 Loans/Borrowings 1. Financial Corporations (a+b) 289781.5 334879.7 453916.6 517087.5 1595665.3 (a) Banking Sector 234235.0 263450.2 370782.9 383843.2 1252311.4 of which: i. Commercial Banks 230283.8 261265.3 368304.6 331291.0 1191144.8 (b) Other Financial Institutions 55546.4 71429.5 83133.7 133244.3 343353.9 i. Non-Banking Financial Companies 30531.7 36650.3 55791.7 94565.3 217539.1 ii. Housing Finance Companies 22336.7 33031.2 24903.3 36745.8 117017.0 iii. Insurance Corporations 2678.0 1747.9 2438.7 1933.2 8797.8 2. Non-Financial Corporations (Private Corporate Business) 33.7 33.7 33.7 33.7 135.0 3. General Government 204.5 204.5 204.5 204.5 818.0 RBI Bulletin November 2025 175CURRENT STATISTICS No. 51 (a): Flow of Financial Assets and Liabilities of Households - Instrument-wise (Contd.) (Amount in ` Crore) 2023-24 Item Q1 Q2 Q3 Q4 Annual Net Financial Assets (I-II) 349607.1 283994.4 294431.6 666547.4 1594580.4 Per cent of GDP 4.8 3.9 3.8 8.4 5.3 I. Financial Assets 671244.1 810128.8 805066.2 1187279.1 3473718.2 Per cent of GDP 9.3 11.2 10.4 14.9 11.5 of which: 1.Total Deposits (a+b) 266680.3 407948.0 296931.3 406706.9 1378266.4 (a) Bank Deposits 253004.1 501768.5 277432.0 390720.4 1422924.9 i. Commercial Banks 243833.9 502260.7 280096.7 383460.6 1409651.9 ii. Co-operative Banks 9170.2 -492.2 -2664.7 7259.8 13273.0 (b) Non-Bank Deposits 13676.2 -93820.5 19499.4 15986.5 -44658.5 of which: Other Financial Institutions (i+ii) -485.4 -107982.1 5337.7 1824.9 -101304.9 i. Non-Banking Financial Companies 6119.3 4782.3 4895.8 1942.9 17740.3 ii. Housing Finance Companies -6604.7 -112764.4 441.9 -118.0 -119045.2 2. Life Insurance Funds 157301.9 140356.8 160135.2 189267.6 647061.4 3. Provident and Pension Funds (including PPF) 163686.0 148356.1 153435.1 253882.9 719360.2 4. Currency -48636.2 -36700.8 56719.0 146643.8 118025.7 5. Investments 41014.3 72664.6 79238.2 108336.6 301253.8 of which: (a) Mutual Funds 32085.6 55768.8 60134.6 90973.0 238962.1 (b) Equity 3756.7 7146.3 9941.1 8236.1 29080.1 6. Small Savings (excluding PPF) 91197.8 77504.1 58607.4 82441.4 309750.7 II. Financial Liabilities 321637.1 526134.4 510634.6 520731.7 1879137.8 Per cent of GDP 4.5 7.3 6.6 6.5 6.2 Loans/Borrowings 1. Financial Corporations (a+b) 321519.8 526016.2 510516.4 520613.5 1878665.8 (a) Banking Sector 213606.3 868873.9 402647.1 392330.5 1877457.7 of which: i. Commercial Banks 208026.5 875654.0 389898.0 382557.9 1856136.4 (b) Other Financial Institutions 107913.6 -342857.7 107869.2 128283.0 1208.0 i. Non-Banking Financial Companies 81448.8 59683.7 85031.8 100836.5 327000.7 ii. Housing Finance Companies 23784.0 -404294.0 21233.4 25852.9 -333423.7 iii. Insurance Corporations 2680.7 1752.6 1604.0 1593.6 7631.0 2. Non-Financial Corporations (Private Corporate Business) 33.7 34.7 34.7 34.7 138.0 3. General Government 83.5 83.5 83.5 83.5 334.0 176 RBI Bulletin November 2025CURRENT STATISTICS No. 51 (a): Flow of Financial Assets and Liabilities of Households - Instrument-wise (Concld.) (Amount in ` Crore) 2024-25 Item Q1 Q2 Q3 Q4 Annual Net Financial Assets (I-II) 551994.2 496676.1 271043.1 674489.0 1994202.4 Per cent of GDP 7.0 6.3 3.2 7.6 6.0 I. Financial Assets 840665.3 901135.4 689663.5 1129381.1 3560845.4 Per cent of GDP 10.6 11.5 8.1 12.8 10.8 of which: 1.Total Deposits (a+b) 274567.9 403591.4 158320.8 418183.6 1254663.6 (a) Bank Deposits 254885.4 388328.6 141290.0 401577.5 1186081.4 i. Commercial Banks 251171.1 389734.0 147864.7 395337.4 1184107.2 ii. Co-operative Banks 3714.3 -1405.4 -6574.7 6240.0 1974.2 (b) Non-Bank Deposits 19682.4 15262.8 17030.8 16606.1 68582.2 of which: Other Financial Institutions (i+ii) 7461.4 3041.8 4809.8 4385.1 19698.2 i. Non-Banking Financial Companies 6289.7 3230.0 4444.5 4220.0 18184.2 ii. Housing Finance Companies 1171.7 -188.2 365.4 165.1 1514.0 2. Life Insurance Funds 175427.0 178835.2 90159.4 90393.0 534814.6 3. Provident and Pension Funds (including PPF) 170218.2 170219.6 170758.3 281332.6 792528.6 4. Currency 34212.5 -57615.2 70840.8 162236.1 209674.1 5. Investments 120638.2 152637.1 159255.2 103720.8 536251.4 of which: (a) Mutual Funds 106987.0 137618.0 124132.0 97193.0 465930.0 (b) Equity 14448.0 15645.0 36063.1 7410.3 73566.5 6. Small Savings (excluding PPF) 65601.6 53467.4 40329.0 73515.0 232913.0 II. Financial Liabilities 288671.1 404459.3 418620.4 454892.1 1566642.9 Per cent of GDP 3.7 5.2 4.9 5.2 4.7 Loans/Borrowings 1. Financial Corporations (a+b) 288492.4 404280.6 418441.7 454713.3 1565928.0 (a) Banking Sector 205040.4 322147.7 319626.6 387045.6 1233860.3 of which: i. Commercial Banks 208525.3 321241.4 302569.3 379856.5 1212192.4 (b) Other Financial Institutions 83452.0 82132.9 98815.0 67667.7 332067.7 i. Non-Banking Financial Companies 65813.7 65488.7 75764.5 39833.9 246900.8 ii. Housing Finance Companies 15125.2 14233.6 20561.4 25756.8 75677.0 iii. Insurance Corporations 2513.1 2410.7 2489.1 2077.1 9489.9 2. Non-Financial Corporations (Private Corporate Business) 34.7 34.7 34.7 34.7 139.0 3. General Government 144.0 144.0 144.0 144.0 576.0 Notes : 1. Net Financial Savings of households refer to the net financial assets, which are measured as difference of financial asset and liabilities flows. 2. Preliminary estimates for 2024-25 and revised estimates for 2022-23 and 2023-24. 3. The preliminary estimates for 2024-25 will undergo revision with the release of first revised estimates of national income, consumption expenditure, savings, and capital formation, 2024-25 by the NSO. 4. Non-bank deposits apart from other financial institutions, comprises state power utilities, co-operative non credit societies etc. 5. Figures in the columns may not add up to the total due to rounding off. RBI Bulletin November 2025 177CURRENT STATISTICS No. 51 (b): Stocks of Financial Assets and Liabilities of Households- Select Indicators (Amount in ` Crore) Item Jun-2022 Sep-2022 Dec-2022 Mar-2023 Financial Assets (a+b+c+d+e+f+g+h) 25621348.1 26423992.1 27187715.6 27844981.1 Per cent of GDP 102.8 102.6 103.3 103.5 (a) Bank Deposits (i+ii) 11843527.1 12143059.7 12399459.4 12707326.2 i. Commercial Banks 10987692.1 11288257.2 11536717.0 11821685.0 ii. Co-operative Banks 855834.9 854802.6 862742.4 885641.2 (b) Non-Bank Deposits of which: Other Financial Institutions 216170.0 218246.7 226328.2 228562.2 i. Non-Banking Financial Companies 74794.2 78061.4 81308.3 85254.0 ii. Housing Finance Companies 141375.8 140185.3 145020.0 143308.2 (c) Life Insurance Funds 5325967.3 5559681.9 5786592.6 5795430.6 (d) Currency 2950343.2 2895763.9 2972524.0 3121514.1 (e) Mutual funds 2048097.3 2260209.7 2355315.8 2367792.5 (f) Public Provident Fund (PPF) 851913.4 858591.1 864730.6 939449.0 (g) Pension Funds 744459.2 796454.0 853412.0 898343.0 (h) Small Savings (excluding PPF) 1640870.6 1691985.1 1729352.9 1786563.5 Financial Liabilities (a+b) 8911860.9 9246740.6 9700657.2 10217744.7 Per cent of GDP 35.8 35.9 36.9 38.0 Loans/Borrowings (a) Banking Sector 7095467.7 7358918.0 7729700.9 8113544.1 of which: i. Commercial Banks 6620073.1 6881338.5 7249643.0 7580934.1 ii. Co-operative Banks 473897.0 476024.8 478486.9 530915.0 (b) Other Financial Institutions 1816393.1 1887822.6 1970956.3 2104200.7 of which: i. Non-Banking Financial Companies 869174.9 905825.3 961617.0 1056182.3 ii. Housing Finance Companies 835181.3 868212.5 893115.8 929861.7 iii. Insurance Corporations 112036.9 113784.8 116223.5 118156.7 178 RBI Bulletin November 2025CURRENT STATISTICS No. 51 (b): Stocks of Financial Assets and Liabilities of Households- Select Indicators (Contd.) (Amount in ` Crore) Item Jun-2023 Sep-2023 Dec-2023 Mar-2024 Financial Assets (a+b+c+d+e+f+g+h) 28754605.9 29637615.0 30737884.8 32025210.0 Per cent of GDP 104.2 104.4 105.0 106.3 (a) Bank Deposits (i+ii) 12960330.3 13462098.8 13739530.7 14130251.1 i. Commercial Banks 12065518.9 12567779.6 12847876.2 13231336.9 ii. Co-operative Banks 894811.4 894319.2 891654.5 898914.3 (b) Non-Bank Deposits of which: Other Financial Institutions 228076.8 120094.7 125432.4 127257.3 i. Non-Banking Financial Companies 91373.3 96155.6 101051.4 102994.3 ii. Housing Finance Companies 136703.5 23939.1 24381.0 24263.0 (c) Life Insurance Funds 6064436.9 6255801.1 6553726.0 6820611.8 (d) Currency 3072877.9 3036177.0 3092896.0 3239539.8 (e) Mutual funds 2626046.1 2829859.3 3156299.3 3387208.3 (f) Public Provident Fund (PPF) 955060.6 960343.6 964851.5 1051376.5 (g) Pension Funds 970016.0 1017975.0 1091276.0 1172651.0 (h) Small Savings (excluding PPF) 1877761.2 1955265.4 2013872.8 2096314.2 Financial Liabilities (a+b) 10539264.5 11065280.7 11575797.1 12096410.5 Per cent of GDP 38.2 39.0 39.6 40.2 Loans/Borrowings (a) Banking Sector 8327150.3 9196024.2 9598671.3 9991001.8 of which: i. Commercial Banks 7788960.6 8664614.6 9054512.6 9437070.5 ii. Co-operative Banks 536409.2 529527.7 542240.6 551852.1 (b) Other Financial Institutions 2212114.2 1869256.5 1977125.7 2105408.7 of which: i. Non-Banking Financial Companies 1137631.1 1197314.8 1282346.6 1383183.0 ii. Housing Finance Companies 953645.7 549351.7 570585.1 596438.0 iii. Insurance Corporations 120837.4 122590.0 124194.0 125787.7 RBI Bulletin November 2025 179CURRENT STATISTICS No. 51 (b): Stocks of Financial Assets and Liabilities of Households- Select Indicators (Concld.) (Amount in ` Crore) Item Jun-2024 Sep-2024 Dec-2024 Mar-2025 Financial Assets (a+b+c+d+e+f+g+h) 33253098.6 34421189.5 34532805.6 35264710.9 Per cent of GDP 107.9 109.6 107.2 106.6 (a) Bank Deposits (i+ii) 14385136.5 14773465.1 14914755.1 15316332.6 i. Commercial Banks 13482508.0 13872242.0 14020106.6 14415444.1 ii. Co-operative Banks 902628.6 901223.2 894648.5 900888.5 (b) Non-Bank Deposits of which: Other Financial Institutions 134718.7 137760.5 142570.3 146955.5 i. Non-Banking Financial Companies 109284.0 112514.0 116958.5 121178.5 ii. Housing Finance Companies 25434.7 25246.5 25611.9 25777.0 (c) Life Insurance Funds 7123527.6 7385938.1 7272871.3 7293099.1 (d) Currency 3273752.3 3216137.1 3286977.8 3449213.9 (e) Mutual funds 3866386.1 4291914.4 4224091.7 4128924.5 (f) Public Provident Fund (PPF) 1059829.5 1063056.1 1064212.0 1157449.2 (g) Pension Funds 1247832.0 1337535.0 1371615.0 1443509.0 (h) Small Savings (excluding PPF) 2161915.8 2215383.2 2255712.2 2329227.2 Financial Liabilities (a+b) 12384902.9 12789183.5 13207625.1 13662338.5 Per cent of GDP 40.2 40.7 41.0 41.3 Loans/Borrowings (a) Banking Sector 10196042.2 10518189.9 10837816.5 11224862.1 of which: i. Commercial Banks 9645595.7 9966837.1 10269406.4 10649262.8 ii. Co-operative Banks 548284.4 549069.4 566104.4 573131.8 (b) Other Financial Institutions 2188860.7 2270993.6 2369808.7 2437476.4 of which: i. Non-Banking Financial Companies 1448996.8 1514485.5 1590250.0 1630083.9 ii. Housing Finance Companies 611563.2 625796.8 646358.2 672115.0 iii. Insurance Corporations 128300.7 130711.4 133200.5 135277.5 Notes : 1. Data as ratios to GDP have been calculated based on the Provisional Estimates of National Income 2024-25, released by NSO on May 30, 2025. 2. Pension funds comprises funds with the National Pension Scheme. 3. Outstanding deposits with Small Savings are sourced from the Controller General of Accounts, Government of India. 4. Non-bank deposits apart from other financial institutions, comprises state power utilities, co-operative non credit societies etc. Data for outstanding deposits are available only for other financial institutions. 5. Figures in the columns may not add up to the total due to rounding off. 180 RBI Bulletin November 2025CURRENT STATISTICS Explanatory Notes to the Current Statistics Table No. 1 1.2& 6: Annual data are average of months. 3.5 & 3.7: Relate to ratios of increments over financial year so far. 4.1 to 4.4, 4.8,4.9 &5: Relate to the last friday of the month/financial year. 4.5, 4.6 & 4.7: Relate to five major banks on the last Friday of the month/financial year. 4.10 to 4.12: Relate to the last auction day of the month/financial year. 4.13: Relate to last day of the month/ financial year 7.1&7.2: Relate to Foreign trade in US Dollar. Table No. 2 2.1.2: Include paid-up capital, reserve fund and Long-Term Operations Funds. 2.2.2: Include cash, fixed deposits and short-term securities/bonds, e.g., issued by IIFC (UK). Table No. 4 Maturity-wise position of outstanding forward contracts is available at http://nsdp.rbi.org.in under ‘‘Reserves Template’’. Table No. 5 Special refinance facility to Others, i.e. to the EXIM Bank, is closed since March 31, 2013. Table No. 6 For scheduled banks, March-end data pertain to the last reporting Friday. 1.1: Notes in Circulation include CBDC-Retail (R) and CBDC-Wholesale (W). 1.4: Cash on Hand with Banks includes CBDC-W. 2.2: Exclude balances held in IMF Account No.1, RBI employees’ provident fund, pension fund, gratuity and superannuation fund. Table Nos. 7 & 11 3.1 in Table 7 and 2.4 in Table 11: Include foreign currency denominated bonds issued by IIFC (UK). Table No. 8 NM and NM do not include FCNR (B) deposits. 2 3 2.4: Consist of paid-up capital and reserves. 2.5: includes other demand and time liabilities of the banking system. Table No. 9 Financial institutions comprise EXIM Bank, SIDBI, NABARD and NHB. L and L are compiled monthly and L quarterly. 1 2 3 Wherever data are not available, the last available data have been repeated. Table No. 13 Data against column Nos. (1), (2) & (3) are Final and for column Nos. (4) & (5) data are Provisional. RBI Bulletin November 2025 181CURRENT STATISTICS Table No. 14 Data in column Nos. (4) & (8) are Provisional. Table No. 17 2.1.1: Exclude reserve fund maintained by co-operative societies with State Co-operative Banks 2.1.2: Exclude borrowings from RBI, SBI, IDBI, NABARD, notified banks and State Governments. 4: Include borrowings from IDBI and NABARD. Table No. 24 Primary Dealers (PDs) include banks undertaking PD business. Table No. 30 Exclude private placement and offer for sale. 1: Exclude bonus shares. 2: Include cumulative convertible preference shares and equi-preference shares. Table No. 32 Exclude investment in foreign currency denominated bonds issued by IIFC (UK), SDRs transferred by Government of India to RBI and foreign currency received under SAARC and ACU currency swap arrangements. Foreign currency assets in US dollar take into account appreciation/depreciation of non-US currencies (such as Euro, Sterling, Yen and Australian Dollar) held in reserves. Foreign exchange holdings are converted into rupees at rupee-US dollar RBI holding rates. Table No. 34 1.1.1.1.2 & 1.1.1.1.1.4: Estimates. 1.1.1.2: Estimates for latest months. ‘Other capital’ pertains to debt transactions between parent and subsidiaries/branches of FDI enterprises. Data may not tally with the BoP data due to lag in reporting. Table No. 35 1.10: Include items such as subscription to journals, maintenance of investment abroad, student loan repayments and credit card payments. Table No. 36 Increase in indices indicates appreciation of rupee and vice versa. For 6-Currency index, base year 2022-23 is a moving one, which gets updated every year. REER figures are based on Consumer Price Index (combined). The details on methodology used for compilation of NEER/REER indices are available in December 2005, April 2014 and January 2021 issues of the RBI Bulletin. Table No. 37 Based on applications for ECB/Foreign Currency Convertible Bonds (FCCBs) which have been allotted loan registration number during the period. 182 RBI Bulletin November 2025CURRENT STATISTICS Table Nos. 38, 39, 40 & 41 Explanatory notes on these tables are available in December issue of RBI Bulletin, 2012. Table No. 43 Part I-A. Settlement systems 1.1.3: Tri- party Repo under the securities segment has been operationalised from November 05, 2018. Part I-B. Payments systems 4.1.2: ‘Others’ includes e-commerce transactions and digital bill payments through ATMs, etc. 4.2.2: ‘Others’ includes e-commerce transactions, card to card transfers and digital bill payments through ATMs, etc. 5: Available from December 2010. 5.1: includes purchase of goods and services and fund transfer through wallets. 5.2.2: includes usage of PPI Cards for online transactions and other transactions. 6.1: Pertain to three grids – Mumbai, New Delhi and Chennai. 6.2: ‘Others’ comprises of Non-MICR transactions which pertains to clearing houses managed by 21 banks. Part II-A. Other payment channels 1: Mobile Payments – Include transactions done through mobile apps of banks and UPI apps. o The data from July 2017 includes only individual payments and corporate payments initiated, o processed, and authorised using mobile device. Other corporate payments which are not initiated, processed, and authorised using mobile device are excluded. 2: Internet Payments – includes only e-commerce transactions through ‘netbanking’ and any financial transaction using internet banking website of the bank. Part II-B. ATMs 3.3 and 4.2: only relates to transactions using bank issued PPIs. Part III. Payment systems infrastructure 3: Includes ATMs deployed by Scheduled Commercial Banks (SCBs) and White Label ATM Operators (WLAOs). WLAs are included from April 2014 onwards. Table No. 45 (-) represents nil or negligible The table format is revised since monthly Bulletin for the month of June 2023. Central Government Dated Securities include special securities and Sovereign Gold Bonds. State Government Securities include special bonds issued under Ujwal DISCOM Assurance Yojana (UDAY). Bank PDs are clubbed under Commercial Banks. The category ‘Others’ comprises State Governments, DICGC, PSUs, Trusts, Foreign Central Banks, HUF/ Individuals etc. Data since September 2023 includes the impact of the merger of a non-bank with a bank. RBI Bulletin November 2025 183CURRENT STATISTICS Table No. 46 GDP data is based on 2011-12 base. GDP for 2023-24 is from Union Budget 2023-24. Data pertains to all States and Union Territories. 1 & 2: Data are net of repayments of the Central Government (including repayments to the NSSF) and State Governments. 1.3: Represents compensation and assignments by States to local bodies and Panchayati Raj institutions. 2: Data are net of variation in cash balances of the Central and State Governments and includes borrowing receipts of the Central and State Governments. 3A.1.1: Data as per RBI records. 3B.1.1: Borrowings through dated securities. 3B.1.2: Represent net investment in Central and State Governments’ special securities by the National Small Savings Fund (NSSF). This data may vary from previous publications due to adjustments across components with availability of new data. 3B.1.6: Include Ways and Means Advances by the Centre to the State Governments. 3B.1.7: Include Treasury Bills, loans from financial institutions, insurance and pension funds, remittances, cash balance investment account. Table No. 47 SDF is availed by State Governments against the collateral of Consolidated Sinking Fund (CSF), Guarantee Redemption Fund (GRF) & Auction Treasury Bills (ATBs) balances and other investments in government securities. WMA is advance by Reserve Bank of India to State Governments for meeting temporary cash mismatches. OD is advanced to State Governments beyond their WMA limits. Average amount Availed is the total accommodation (SDF/WMA/OD) availed divided by number of days for which accommodation was extended during the month. - : Nil. Table No. 48 CSF and GRF are reserve funds maintained by some State Governments with the Reserve Bank of India. ATBs include Treasury bills of 91 days, 182 days and 364 days invested by State Governments in the primary market. --: Not Applicable (not a member of the scheme). The concepts and methodologies for Current Statistics are available in Comprehensive Guide for Current Statistics of the RBI Monthly Bulletin (https://rbi.org.in/Scripts/PublicationsView.aspx?id=17618) Time series data of ‘Current Statistics’ is available at https://data.rbi.org.in. Detailed explanatory notes are available in the relevant press releases issued by RBI and other publications/releases of the Bank such as Handbook of Statistics on the Indian Economy. 184 RBI Bulletin November 2025RREECCEENNTT PPUUBBLLIICCAATTIIOONNSS Recent Publications of the Reserve Bank of India Name of Publication Price India Abroad 1. Reserve Bank of India Bulletin2025 `350 per copy US$ 15 per copy `250 per copy (concessional rate*) US$ 150 (one-year subscription) `4,000 (one year subscription) (inclusive of air mail courier charges) `3,000 (one year concessional rate*) 2. Handbook of Statistics on theIndian `550 (Normal) US$ 24 States 2023-24 `600 (inclusive of postage) (inclusive of air mail courier charges) 3. Handbook of Statistics on theIndian `600 (Normal) US$ 50 Economy 2024-25 `650 (inclusive of postage) (inclusive of air mail courier charges) `450 (concessional) `500 (concessional with postage) 4. State Finances - `600 per copy (over the counter) US$ 24 per copy A Study of Budgets of 2024-25 `650 per copy (inclusive of postal charges) (inclusive of air mail courier charges) 5. Report on Currency and Finance `575 per copy (over the counter) US$ 22 per copy 2023-24 `625 per copy (inclusive of postal charges) (inclusive of air mail courier charges) 6. Reserve Bank of India `200 per copy (over the counter) US$ 18 per copy Occasional Papers Vol. 45, No. 1, 2024 `250 per copy (inclusive of postal charges) (inclusive of air mail courier charges) 7. Finances of Panchayati Raj Institutions `300 per copy (over the counter) US$ 16 per copy `350 per copy (inclusive of postal charges) (inclusive of air mail courier charges) 8. Report on Trend and Progress of Issued as Supplement to RBI Bulletin Banking in India 2023-24 January, 2025 9. Annual Report 2024-25 Issued as Supplement to RBI Bulletin June, 2025 10. Financial Stability Report, Issued as Supplement to RBI Bulletin June 2025 July, 2025 11. Monetary Policy Report - October 2025 Included in RBI Bulletin October 2025 12. Report on Municipal Finances - `300 per copy (over the counter) US$ 16 per copy November 2024 `350 per copy (inclusive of postal charges) (inclusive of air mail courier charges) 13. Banking Glossary (English-Hindi) `100 per copy (over the counter) `150 per copy (inclusive of postal charges) Notes 1. Many of the above publications are available at the RBI website (www.rbi.org.in). 2. Time Series data are available at the Database on Indian Economy (https://data.rbi.org.in). 3. The Reserve Bank of India History 1935-2008 (5 Volumes) are available at leading book stores in India. * Concession is available for students, teachers/lecturers, academic/education institutions, public libraries and Booksellers in India provided the proof of eligibility is submitted. RBI Bulletin November 2025 185RREECCEENNTT PPUUBBLLIICCAATTIIOONNSS General Instructions 1. All communications should be addressed to: Director, Division of Reports and Knowledge Dissemination, Department of Economic and Policy Research (DRKD, DEPR), Reserve Bank of India, Amar Building, Ground Floor, Sir P. M. Road, Fort, P. B. No.1036, Mumbai - 400 001. Telephone: 022- 2260 3000 Extn: 4002, Email: spsdepr@rbi.org.in. 2. Publications are available for sale between 10:30 am to 3:00 pm (Monday to Friday). 3. Publications will not be supplied on a cash-on-delivery basis. 4. Publications once sold will not be taken back. 5. Back issues of the publication are generally not available. 6. Wherever concessional price is not indicated, a discount of 25 per cent is available for students, faculty, academic/education institutions, public libraries, and book sellers in India provided the proof of eligibility is submitted. 7. Subscription should be made preferably by NEFT and transaction details including payer’s name, subscription number (if any), account number, date and amount should be emailed to spsdepr@rbi.org.in, or sent by post. a. Details required for NEFT transfer are as follows: Beneficiary Name Department of Economic and Policy Research, RBI Name of the Bank Reserve Bank of India Branch and address Fort, Mumbai IFSC of Bank Branch RBIS0MBPA04 Type of Account Current Account Account Number 41-8024129-19 b. In case of subscription through non-digital modes, please send the demand draft/cheque payable at Mumbai in favour of Reserve Bank of India, Mumbai. 8. Complaints regarding ‘non-receipt of publication’ may be sent within a period of two months. 186 RBI Bulletin November 2025