Global imbalances denote the distribution of countries’ current account balances, identically equal to the difference between two forward-looking aggregate variables: national savings and domestic investment. Industrial and trade policies have traditionally not been considered important drivers of aggregate savings or investment, and therefore of current account balances. The former because most industrial policies are small in scope; the latter because permanent tariffs have no intertemporal effect in the textbook model, with an offsetting appreciation of the real exchange rate. The rapidly growing use of both industrial and trade policies in recent years calls for a reassessment. This paper presents a framework to think about the role of both policies. For industrial policy, we make the important distinction between the traditional sector-specific policies via subsidies or other targeted instruments (‘micro IP’) and broader policies (‘macro IP’) that aim to promote industrial developments and competitiveness through the deployment of more aggregate instruments such as financial repression, foreign reserve accumulation, or capital controls. A key finding is that ‘micro IP’ tends to increase external balances if it fails to raise aggregate productivity. By contrast, ‘macro IP’ can, under some conditions, boost the current account, forcing other countries to adjust. Yet, these policies often come at the cost of suppressed domestic consumption and possibly domestic welfare. Our analysis confirms that tariffs are a weak tool to improve current account balances. Finally, traditional macroeconomic drivers—such as fiscal policy, demographics or credit cycles—remain critical drivers of global imbalances, especially for the US and China.

Subject. The credit cycle in the Russian economy. Objectives. To characterize the credit cycle in the Russian economy. Methods. The research is based on the application of special economic and mathematical methods of statistical and visual data analysis, construction of trend models and estimation of approximation coefficients to determine the strength of trends. Results. A significant credit expansion has been revealed, during which the total debt of non-financial organizations and households has more than doubled. Cyclical patterns have been found within the corporate lending market, manifested in three phases: rapid growth in lending to small and medium-sized businesses; stabilization; and a shift in focus to lending to large businesses (legal entities and individual entrepreneurs). It was found that the traditional phases of the credit cycle (expansion, overheating, and contraction) were indistinctly expressed. The expansion phase was forced and state-induced. The overheating phase manifested itself not in a halt in growth, but in the accumulation of debt imbalances and the growing vulnerability of the economy. The classic compression phase was suppressed and postponed due to the continuation of government support programs and the specific business response to the high key rate. It was determined that the debt growth was heterogeneous: from "crisis" to "adaptive" and "inflationary". The credit cycle was transformed from a classical market model into a system of managed credit expansion, where the state was a key element that replaced market mechanisms. This has led to the accumulation of significant structural imbalances. Conclusions. The research is relevant for applied economics and finance, and its value lies not only in stating facts, but also in formulating warnings about systemic risks for key stakeholders in the Russian economy.

This paper examines the intersection of climate-induced shocks, financial frictions, and systemic risk transmission in Barbados, a small island economy heavily reliant on a bank-centric financial system. It explores how climate shocks exacerbate non-performing loans (NPLs), intensify financial instability, and impede economic recovery. By integrating financial stability theory and climate finance, this study highlights the persistence of credit supply constraints in Barbados, driven by risk-averse banking behavior, path-dependent lending, and macroeconomic rigidities. The research demonstrates that extreme climate events disproportionately amplify systemic risk, with a nonlinear relationship between capital destruction and financial stress. Using a comprehensive macro-financial stress-testing framework, the study assesses the impact of climate-induced capital loss on capital adequacy and credit supply, revealing significant vulnerabilities in the financial system. The findings challenge conventional credit cycle models by showing that supply-side constraints dominate over demand-driven factors. The paper also discusses the role of regulatory frameworks, suggesting the need for climate-adjusted stress testing, improved provisioning mechanisms, and enhanced financial resilience policies to safeguard economic stability. The study offers a roadmap for policymakers to strengthen the financial system’s resilience to climate risks and improve capital allocation for long-term productivity growth.

The Global Financial Crisis has revived the concept of macro-prudential regulation and has given way to the introduction of many new instruments. Today, the framework stands as an overarching public policy that aims towards achievement of financial stability across the globe. It has become an effective tool to combat the imbalances that arise due to interconnected balance sheets of financial institutions and has emerged as a complement to the traditional micro-prudential regulatory apparatus. The framework has exclusive measures to counter any unprecedent growth in credit, liquidity and capital components of the financial intermediaries. The present study discusses these measures rigorously. Many Emerging Market Economies have been using this toolkit since 1997 and have hence stayed insulated against the repercussions of the global recession. India, in particular, has a long-standing experience with the operation of the policy instruments particularly to contain the credit cycle and mitigate the systemic tendency of any financial risk. It has witnessed the exercise of the policy framework without any conflict with its macroeconomic goals like price stability and GDP growth. However, macroprudential policy is an infant regulatory framework. So, policy makers should take into account the limitations of the policy and make it work in conjunction with other major policies for effective functioning of an economy.

Credit cycles, lending shocks, and the business cycle

This paper extends the literature studying the prediction of financial crises in two ways, namely by: (i) developing a new text-based indicator measuring banks' sentiment tailored to the context of financial stability, and (ii) applying machine learning (ML) techniques to predict systemic crises in the euro area as defined by the European Systemic Risk Board (ESRB). In-sample analysis indicates that banks' financial stability sentiment (BFSS) is a highly statistically significant predictor of systemic crises, with a negative one standard deviation shock in the BFSS indicator corresponding to increases in the probability of a systemic crisis of 7 and 3 percentage points one-quarter and four-quarters ahead, respectively, while controlling for the credit cycle. Out-of-sample results show that, while the BFSS tends to improve the predictive performance of baseline logistic regression models, ML models grounded in financial stability dictionaries deliver substantially higher predictive accuracy in forecasting systemic crises. By improving the accuracy and timeliness of systemic crisis prediction, this novel application can be useful to complement conventional approaches for calibrating macroprudential policy tools and enhance crisis prevention frameworks.

ABSTRACT This paper empirically assesses how effective macroprudential policies are at preventing and mitigating excessive procyclicality for credit, and whether their effectiveness is driven by how such policies are conducted over the business cycle. We use a sample of 42 OECD and non‐OECD countries over the period 1990Q1–2019Q4 and propose an original macroprudential policy stance index that gauges the degree of countercyclicality of a policy, and we estimate whether it is an important determinant of credit procyclicality. Our results are based on an IPVAR model and confirm that the intensity of credit procyclicality decreases significantly as the degree of countercyclicality of the macroprudential policy increases. We find that the credit cycle responds less to a business cycle shock when the macroprudential policy is conducted in a countercyclical way. Consequently, our empirical findings highlight that the key to making macroprudential policies effective is the art of moving instruments in the right direction at the right time.

Abstract This study evaluates how ESG strategic intensity and AI capability shape risk-adjusted lending performance in ASEAN-5 banks, grounding the model in the Resource-Based View (RBV) and Institutional Theory to explain how internal capabilities and external regulatory forces interact. Using a cross-sectional survey of 486 banking professionals from 62 listed commercial banks across Indonesia, Malaysia, the Philippines, Singapore, and Thailand, relationships were estimated via PLS-SEM with standard robustness checks, including multicollinearity, reliability, validity, and predictive relevance, while mediation and moderation were assessed through bootstrapped indirect effects and interaction terms. The results show that ESG strategic intensity directly improves risk-adjusted lending performance, while AI capability influences performance only indirectly. Both ESG and AI strongly enhance credit-risk discipline, which itself is a key driver of lending performance. ESG retains a direct path to performance while also working through credit-risk discipline, its effect reflects partial mediation. In contrast, the effect of AI operates entirely through credit-risk discipline, indicating full mediation. Regulatory pressure strengthens the influence of both ESG and AI on credit-risk discipline, demonstrating that stricter supervisory environments amplify the translation of sustainability and technological capabilities into more disciplined lending practices. These findings underscore CRD as the operational hinge through which sustainability and digital capabilities are converted into superior lending outcomes, highlighting the importance of governed data pipelines, explainable risk models, and effective early-warning mechanisms, supported by aligned managerial incentives and supervisory expectations. At the societal level, disciplined ESG- and AI-enabled lending reduces information frictions, supports equitable credit access for credible SMEs and households, stabilizes credit cycles, and mitigates adverse selection when paired with safeguards on privacy, transparency, and bias control. Overall, the study offers an integrated, theory-driven, institution-level assessment of how ESG and AI capabilities translate into measurable performance within a multi-country ASEAN context, clarifying when and how strategic and regulatory forces jointly improve credit outcomes.

This paper investigates the external drivers of Cambodia’s credit cycles using a dynamic factor model. Results indicate that common global and regional factors explain over 60 percent of credit growth variance, reflecting one of Asia’s highest sensitivities to external conditions. The analysis reveals that external shocks, including US monetary policy, global risk sentiment and China’s growth, transmit primarily through a common global credit channel rather than direct bilateral linkages. This high exposure to the global financial cycle highlights the necessity of macroprudential policies to manage domestic volatility in Cambodia’s highly dollarized economy.

Abstract Procyclicality in the banking sector is one of the important indicators that may encourage the systemic risk in the banking system. This study examines banking behavior of the economy during the COVID-19 pandemic and analyze the amplitude and frequency of the credit cycle of Islamic and conventional banks. This study is primarily focused on the credit property of Islamic and conventional banks from 2014 to 2020 with application of Ordinary Least Square (OLS), Frequency Base Filter Analysis (FBF) and Turning Point Analysis. Our study finds that the size of Islamic bank’s amplitude is larger than the size of conventional bank’s amplitude. This is characteristic of Islamic banks based on the pattern of financing of the real sector. Meanwhile, conventional banks encourage the creation of bubble capital because it is related to the credit pattern grounded in speculative activities based on the interest system. Therefore, conventional banks need to encourage credit patterns based on capital. Meanwhile, the size of the frequency of Islamic banks has a longer frequency measure than conventional banks, but the number of cycles formed is the same as a perfect cycle.

How Do Macroprudential Measures Affect Mortgage Lending Standards? Evidence from the ECB’s Bank Lending Survey

Abstract We augment an overlapping generations endogenous credit cycle model with environmental externalities and two regulatory authorities to study how fiscal and financial environmental regulation together shape environmental quality, macroeconomic stability, and income distribution. Environmental quality depends on pollution from the brown sector, regulated either through environmental haircuts on collateral or via tax-financed abatement and environmental improvements. We find that haircuts and taxes affect emissions, income distribution, and system stability in distinct ways, with interaction effects that create trade-offs between environmental outcomes and macroeconomic stability. Compared to scenarios with only financial regulation, introducing an environmental tax maintains similar environmental quality but achieves higher aggregate income and capital per worker. However, we uncover intergenerational trade-offs as environmental regulation improves environmental quality and raises incomes for younger agents and investors but lowers and destabilizes the returns of older generations reliant on capital income.

Abstract Rapid credit expansions predict lower output growth and banking crises, but does it matter who finances them? We identify the ultimate counterparties financing credit expansions in a panel of 33 advanced economies and find that foreign-financed household credit expansions predict lower GDP growth and higher crisis risk, but domestically financed credit expansions do not. Studying the mechanisms, we find that foreign-financed household credit expansions are accompanied by higher supply of foreign capital (reflected in low credit spreads), are followed by elevated credit cycle reversal risk, and lead to higher debt service payments to foreigners which depress aggregate demand. (JEL: E32, E44, F34, G01, G15, G51)

ABSTRACT This study examines the heterogeneous impacts of macroprudential policy across GDP quantiles in Vietnam, considering the roles of the credit cycle, financial cycle and financial leverage. The findings highlight that the impact of the policy is more pronounced during extreme phases of these cycles, operating primarily by moderating their effects through the dominant credit channel. This reveals state-dependent trade-offs in the effects of macroprudential policy. The temporary trade-off between economic downturns and upturns is conditional, as its direction reverses depending on the prevailing financial cycle. Furthermore, while a temporary trade-off exists between different financial cycle phases during economic downturns, the initial direction of this trade-off is reversed during economic upturns and then reverses again in the long term. Crucially, this article identifies specific conditions that diminish the magnitude of all identified short-term trade-offs, while also specifying the circumstances that shorten their duration.

This article addresses the growing problem of financial risk in Ecuadorian savings and credit cooperatives, caused by economic informality, increased delinquency, and weak digitization of credit processes. Given this opportunity for improvement, the purpose of the study is to propose a comprehensive credit cycle model that allows for coordinated, preventive risk management in line with SEPS-2023-0225 regulations. The research was conducted through a systematic documentary, analytical, and descriptive review, integrating Ecuadorian regulations with national and international empirical evidence. Academic studies, specialized theses, and global risk management standards were analyzed, which made it possible to identify gaps between the regulatory framework and the actual practices of cooperatives. The results reveal that the main weaknesses are concentrated in operational fragmentation, lack of digital traceability, and poor coordination between evaluation, monitoring, and collection. The proposed model structures the credit cycle into three stages: granting, focused on technical evaluation and segregation of duties; monitoring, with an emphasis on destination monitoring, portfolio control, and early warnings; and recovery, which incorporates preventive, dynamic, and reactive actions.

The estimation of future loan losses is not only important for the financial institutions to effectively control the credit risk of commercial loan portfolio, but also an essential component in the capital plan submitted for regulatory approval in the annual stress testing. 1 This paper describes a methodology of modeling the credit cycle index and estimating the point-in-time (PIT) default and rating migration probabilities based on credit cycle index under macro-economic scenarios. The modeling approach is based on Credit Metrics (1996) and designed to capture the credit risk concentration at the region and industry sector levels for effective stress testing and risk management of credit portfolios. The credit cycle index 2 in the model represents the “hidden” risk factor underlying the default and credit migration with the asset correlation parameter attributed to the default clustering, while the maximum likelihood estimation (MLE) of credit cycle index and asset correlation provides a compelling explanation for the original design of risk-weight function used in the calculation of credit risk capital charge under Basel advanced capital model. The credit cycle index generated from the model captures the peak and trough of speculative grade default rates over the long-run historical period, which is consistent with the empirical evidence of credit cycle exhibited for each historical downturn during last 30 years. Furthermore, we apply least-square regression techniques to show the credit cycle index is statistically linked to the macro-economic variables, such as GDP, CPI and Unemployment Rate (UR) which are key economic indicators used by economists to analyze and explain the business cycle. The model establishes a powerful connection between credit cycle and economic cycle which enables the risk manager at large financial institutions to perform stress testing and loss forecasting to effectively control the potential credit loss of loan portfolio under various macro-economic scenarios.

This study proposes an innovative and indepth financial statement analysis of Punjab National Bank (PNB), a prominent public sector bank in India, by synergistically integrating traditional financial ratio analysis with advanced Machine Learning (ML) and Deep Learning (DL) techniques. Recognizing the complex and dynamic nature of the Indian banking sector, characterized by evolving credit cycles, stringent regulatory frameworks, and intense competition, conventional financial analysis often provides a retrospective view. This research aims to move beyond static analysis by leveraging the power of AI to unearth deeper insights and generate predictive intelligence.The methodology will involve meticulously collecting and processing PNB's audited financial statements over a significant 5-10 year period (e.g., FY2015-FY2024). A comprehensive set of bank-specific financial ratios will be computed across key dimensions including liquidity (e.g., CreditDeposit Ratio, Liquid Assets to Total Assets), profitability (e.g., Net Interest Margin, Return on Assets, Cost-to-Income Ratio), solvency/capital adequacy (e.g., Capital Adequacy Ratio), and asset quality (e.g., Gross Non-Performing Assets Ratio, Provision Coverage Ratio).Subsequently, ML algorithms such as Random Forest and XGBoost will be employed. These models are adept at identifying intricate, non-linear relationships and will be used to pinpoint the most influential ratios impacting PNB's core profitability and asset quality, thereby revealing hidden drivers and potential risk factors. Furthermore, to provide crucial forward-looking perspectives, Long ShortTerm Memory (LSTM) networks, a specialized form of Deep Learning, will be applied. LSTMs are particularly well-suited for time-series forecasting, enabling the models to capture inherent temporal dependencies, seasonal patterns (e.g., quarterly fluctuations in deposit growth or credit off-take), and market volatilities unique to the banking sector. The performance of these AI-driven forecasting models will be rigorously evaluated against traditional linear time-series models (e.g., ARIMA) using standard metrics like Mean Squared Error (MSE) and prediction accuracy. The expected outcome is to demonstrate how AI significantly enhances predictive accuracy and offers dynamic, data-driven insights, thereby empowering stakeholders with superior tools for strategic decision-making, proactive risk management, and more informed capital allocation within the complex banking landscape.

We study the competitive behavior of the two largest US Banks before and after the financial crisis that started in 2007. The analysis documents that JP Morgan Chase, the initially smaller of the two rivals, followed a competitive strategy aiming to expand its lending to catch up with the leading private lender, the Bank of America. By contrast, the latter bank did not engage in an expansionary push to keep its quantitative advantage. Instead, it opted to diversify its lending portfolio and to invest in technological advances. This assessment is supported by econometric estimates and counterfactual simulations. With its strategy of quality over quantity the Bank of America refrained from engaging in herd behavior which could have led to a new upswing of the credit cycle. JEL classification numbers: D22, E32, E7, G21. Keywords: Herd Behavior, US Banks, Competitive Rivalry, Credit Cycle.

Abstract During the U.S. housing credit boom, publicly traded banks increased mortgage lending activity and relaxed standards much more than privately held banks. The increase in risk had real effects for a variety of county-level aggregates including employment and consumption. Cross-sectional evidence and a quasi-experiment indicate that the increase in risk stemmed from the institutional ownership and the equity compensation of publicly traded banks, in turn leading banks to place greater weight on short-term equity performance. These results are consistent with the view that a focus on short-term earnings and stock prices amplifies boom–bust credit cycles, in turn leading to real cycles for the aggregate economy.

This study examines how credit control disciplines—process conformance, authorization limits, collection oversight, and accounting recognition—shape the stability of interest income in a kelurahan-level microfinance cooperative (Koperasi PMK). Using a descriptive–analytic, quantitative design with secondary financial statements, the analysis connects the cooperative’s Standard Operating Procedures (Institutional and Education SOPs) to the full credit cycle (origination, appraisal, approval, disbursement, collection, remedial) and to recognition policies for performing and non-performing loans. Findings indicate a consistent execution gap: although approval hierarchies, 5C/7C screening, and periodic reviews are formally specified, field-intensive collections and limited information systems delay risk classification and accrual suspension. The absence of a dedicated accrued-interest ledger (PYMAD) and incomplete off-balance-sheet treatment for NPLs create a bias toward overstated interest income during stress, followed by reversals. The study argues that hardening execution—not redesigning policy—yields the highest payoff: enforce status-based recognition (accrual for performing, cash basis for deteriorated), stand up PYMAD and provisioning by collectibility bucket, implement maker–checker and daily receipt–ledger reconciliations in collections, and institutionalize monthly early-warning reviews under board and supervisory oversight. These steps trade short-term reported income for durable, decision-useful interest earnings, aligning sustainability with the cooperative’s outreach mandate.