Banks may be unable to refinance short-term liabilities in case of solvency concerns. To manage this risk, banks can accumulate a buffer of liquid assets, or strengthen transparency to communicate solvency. While a liquidity buffer provides complete insurance against small shocks, transparency covers also large shocks but imperfectly. Due to leverage, an unregulated bank may choose insufficient liquidity buffers and transparency. The regulatory response is constrained: while liquidity buffers can be imposed, transparency is not verifiable. Moreover, liquidity requirements can compromise banks’ transparency choices, and increase refinancing risk. To be effective, liquidity requirements should be complemented by measures that increase bank incentives to adopt transparency.

Traditional theory suggests that higher bank profitability (or franchise value) dissuades bank risk-taking. We highlight an opposite effect: higher profitability loosens bank borrowing constraints. This enables profitable banks to take risk on a larger scale, inducing risk-taking. This effect is more pronounced when bank leverage constraints are looser, or when new investments can be financed with senior funding (such as repos). The model’s predictions are consistent with some notable cross-sectional patterns of bank risk-taking in the run-up to the 2008 crisis.

We study the composition of bank loan portfolios during the transition of the real sector to a knowledge economy where firms increasingly use intangible capital. Exploiting heterogeneity in bank exposure to the compositional shift from tangible to intangible capital, we show that exposed banks curtail commercial lending and reallocate lending to other assets, such as mortgages. We estimate that the substantial growth in intangible capital since the mid-1980s explains around 30% of the secular decline in the share of commercial lending in banks’ loan portfolios. We provide suggestive evidence that this reallocation increased the riskiness of banks’ mortgage lending.

We present a new dataset on the dynamics of non-performing loans (NPLs) during 92 banking crises since 1990. The data show similarities across crises in NPL buildup but much heterogeneity in the pace of NPL resolution. We document how high and unresolved NPLs deepen post-crisis recessions and use a machine learning approach to establish pre-crisis predictors of NPL problems. These predictors—a set of weak macroeconomic, institutional, corporate, and banking sector conditions—help shed light on post-COVID-19 NPL vulnerabilities.

This paper studies the significant variation in the cross-section of standalone and systemic risk of large banks during the recent financial crisis to identify bank specific factors that determine risk. We find that systemic risk grows with bank size and is inversely related to bank capital, and this effect exists above and beyond the effect of bank size and capital on standalone bank risk. Our results contribute to the ongoing debate on the merits of imposing systemic risk-based capital requirements on banks.

This paper offers novel evidence on the impact of raising bank capital requirements on lending in an emerging market and explores heterogeneous effects, depending on bank characteristics and economic conditions. Using quarterly bank-level data and exploiting the adoption of bank-specific capital buffers, we find that higher capital requirements are associated with lower credit growth in Peru. But the effect is short-lived and becomes insignificant in about half a year. The impact of capital requirements varies with economic conditions and bank characteristics. The effects is stronger during periods of lower economic growth. Weaker (less profitable, less capitalized and less liquid) banks react more to changes in capital requirements. Our findings are robust to estimating a variety of specification to address concerns about the endogeneity of capital requirements.