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We study how monetary policy in China influences banks’ shadow banking activities. We develop and estimate the endogenously switching monetary policy rule that is based on institutional facts and at the same time tractable in the spirit of Taylor (1993). This development, along with two newly constructed micro banking datasets, enables us to establish the following empirical evidence. Contractionary monetary policy during 2009–2015 caused shadow banking loans to rise rapidly, offsetting the expected decline of traditional bank loans and hampering the effectiveness of monetary policy on total bank credit. We advance a theoretical explanation of our empirical findings.

We present evidence of a risk-taking channel of monetary policy for the U.S. banking system. We use confidential data on banks' internal ratings on loans to businesses over the period 1997 to 2011 from the Federal Reserve's Survey of Terms of Business Lending. We find that ex ante risk-taking by banks (measured by the risk rating of new loans) is negatively associated with increases in short-term interest rates. This relationship is more pronounced in regions that are less in sync with the nationwide business cycle, and less pronounced for banks with relatively low capital or during periods of financial distress.

Purpose The purpose of this paper is to investigate the relationship between gender diversity and the risk profile of Italian banks during the period 2015–2019. This study examines whether the presence of female board directors or top executives has any significant effect on bank risk-taking. Design/methodology/approach To explore the influence of women on bank risk-taking, the authors analyzed a sample of 387 Italian banks and developed an econometric model applying unbalanced panel data with firm fixed effects and controls per year. Within a multivariate regression model, the authors considered five risk dimensions to verify the effect of gender diversity. Findings The findings suggest that female board directors and executives are considerably more risk averse and less overconfident than their male colleagues, thus confirming a negative causality between risk-taking and gender diversity. The results reveal that banks headed by women are less risky because they report higher capital adequacy and equity to assets ratios. As credit risk in female-led banks is no different from male-led ones, higher capital adequacy does not derive from lower asset quality because it is linked to the higher risk aversion of female directors and top managers. Research limitations/implications From a theoretical standpoint, the results suggest that having women in executive positions entails different risk implications for Italian banks; from a managerial perspective, the results highlight conditions that may promote the role of women in the banking sector. The conclusions are of particular significance because they provide some support for the view that regulators should favor gender quotas in the board management of banks to reduce risk-taking behavior. Originality/value This paper offers an in-depth examination of the risk practices of banks and it attempts to bridge the gap in prior literature on the risk profile of the Italian banking industry given that few empirical studies have examined the determinants of risk-taking in this field, to date. The findings on the higher risk aversion of women directors advance the understanding of the determinants of risk-taking behavior in banks, suggesting that gender quotas in bank boards can contribute to reducing risk-taking behavior. This also unveils some policy implications for bank regulatory authorities.

We analyze the relationship between asset price bubbles and systemic risk, using bank-level data covering almost 30 years. Banks’ systemic risk already rises during a bubble’s buildup and even more so during its bust. The increase in risk strongly differs across banks and by bubble. It depends on bank characteristics (especially bank size) and bubble characteristics and can become very large: in a median real estate bust, systemic risk increases by almost 70% of the median for banks with unfavorable characteristics. These results emphasize the importance of bank-level factors in the buildup of financial fragility during bubble episodes.

We study the allocation of interest rate risk within the European banking sector using novel data. Banks’ exposure to interest rate risk is small on aggregate, but heterogeneous in the cross-section. Contrary to conventional wisdom, net worth is increasing in interest rates for approximately half of the institutions in our sample. Cross-sectional variation in banks’ exposures is driven by cross-country differences in loan-rate fixation conventions for mortgages. Banks use derivatives to partially hedge on-balance-sheet exposures. Residual exposures imply that changes in interest rates have redistributive effects within the banking sector.

Banks face a dilemma in choosing between maximising profits and facilitating the sustainable use of resources within a carbon-constrained future. This study provides empirical evidence on this dilemma, investigating whether a bank loan announcement for a firm with high carbon risk conveys information to investors about the firm's carbon risk exposure collected through a bank's pre-loan screening and ongoing monitoring. We use a sample of 120 bank loan announcements for ASX-listed firms over the period 2009-2015. We measure high (low) carbon risk exposure based on whether firms meet (do not meet) the reporting threshold of the NGER scheme. We document positive and significant excess loan announcement returns for loan renewals for high carbon risk firms, but not for loan initiations. Further, we document a more significant loan announcement return for renewals with favourable term revisions. Finally, we find no evidence that the market differentiates between domestic and foreign lenders. Taken together, our results suggest that investors perceive that banks incorporate carbon risk considerations into their lending decisions. Our results highlight the value of banks as financial intermediaries given the information asymmetry surrounding firms' carbon risk exposure, and more generally the need to extend modern banking theory to consider issues such as the impact of banks' CSR reputation on lending decisions.

We develop a tractable model of banks’ liquidity management with an over-thecounter interbank market to study the credit channel of monetary policy. Deposits circulate randomly across banks and must be settled with reserves. We show how monetary policy affects the banking system by altering the trade-off between profiting from lending and incurring greater liquidity risk. We present two applications of the theory, one involving the connection between the implementation of monetary policy and the pass-through to lending rates, and another considering a quantitative decomposition behind the collapse in bank lending during the 2008 financial crisis. Our analysis underscores the importance of liquidity frictions and the functioning of interbank markets for the conduct of monetary policy.

There is an increasing influence of machine learning in business applications, with many solutions already implemented and many more being explored. Since the global financial crisis, risk management in banks has gained more prominence, and there has been a constant focus around how risks are being detected, measured, reported and managed. Considerable research in academia and industry has focused on the developments in banking and risk management and the current and emerging challenges. This paper, through a review of the available literature seeks to analyse and evaluate machine-learning techniques that have been researched in the context of banking risk management, and to identify areas or problems in risk management that have been inadequately explored and are potential areas for further research. The review has shown that the application of machine learning in the management of banking risks such as credit risk, market risk, operational risk and liquidity risk has been explored; however, it doesn't appear commensurate with the current industry level of focus on both risk management and machine learning. A large number of areas remain in bank risk management that could significantly benefit from the study of how machine learning can be applied to address specific problems.

We develop a Bayesian methodology for systemic risk assessment in financial networks such as the interbank market. Nodes represent participants in the network, and weighted directed edges represent liabilities. Often, for every participant, only the total liabilities and total assets within this network are observable. However, systemic risk assessment needs the individual liabilities. We propose a model for the individual liabilities, which, following a Bayesian approach, we then condition on the observed total liabilities and assets and, potentially, on certain observed individual liabilities. We construct a Gibbs sampler to generate samples from this conditional distribution. These samples can be used in stress testing, giving probabilities for the outcomes of interest. As one application we derive default probabilities of individual banks and discuss their sensitivity with respect to prior information included to model the network. An R package implementing the methodology is provided. This paper was accepted by Noah Gans, stochastic models and simulation .

This paper examines whether bank capital ratios and default risk are associated with the gender of the bank's Chief Executive Officer (CEO) and Chairperson of the board. Given the documented gender-based differences in conservatism and risk tolerance, we postulate that female CEOs and board Chairs should assess risks more conservatively, and thereby hold higher levels of equity capital and reduce the likelihood of bank failure during periods of market stress. Using a large panel of U.S. commercial banks, we document that banks with female CEOs hold more conservative levels of capital after controlling for the bank's asset risk and other attributes. Furthermore, while neither CEO nor Chair gender is related to bank failure in general, we find strong evidence that smaller banks with female CEOs and board Chairs were less likely to fail during the financial crisis. Overall, our findings are consistent with the view that gender-based behavioral differences may affect corporate decisions.