During banking crises, regulators must decide between bailouts or liquidations,neither of which are publicly popular. A comprehensive assessment of regulators,however, requires examining all their decisions against regulators’ objectives of preserving financial stability while discouraging...

The recent financial crisis has focused attention on identifying and measuring systemic risk. In this paper, we propose a novel approach to estimate the portfolio composition of banks as function of daily interbank trades and stock returns. While banks’ assets are reported to regulators...

When capital in the banking system becomes depleted, the degree to which financial intermediation and the macroeconomy are adversely affected is likely to depend on the financial and macroeconomic environment. However, existing studies either assume that the effects of bank capital shocks are...

This paper estimates ordered logit and probit regression models for bank ratings which also include a country index to capture country-specific variation. The empirical findings provide support to the hypothesis that the individual international bank ratings assigned by Fitch Ratings are...

This paper estimates ordered logit and probit regression models for bank ratings which also include a country index to capture country-specific variation. The empirical findings provide support to the hypothesis that the individual international bank ratings assigned by Fitch Ratings are...

This paper estimates ordered logit and probit regression models for bank ratings which also include a country index to capture country-specific variation. The empirical findings provide support to the hypothesis that the individual international bank ratings assigned by Fitch Ratings are...

This paper estimates ordered logit and probit regression models for bank ratings which also include a country index to capture country-specific variation. The empirical findings provide support to the hypothesis that the individual international bank ratings assigned by Fitch Ratings are...

Using novel data and machine learning techniques, we develop an early warning system for bank distress. The main input variables come from confidential regulatory returns, and our measure of distress is derived from supervisory assessments of bank riskiness from 2006 through to 2012. We...

The paper develops an early warning system to identify banks that could face liquidity crises. To obtain a robust system for measuring banks’ liquidity vulnerabilities, we compare the predictive performance of three models – logistic LASSO, random forest and Extreme Gradient Boosting – and...

This paper proposes a framework for deriving early-warning models with optimal out-of-sample forecasting properties and applies it to predicting distress in European banks. The main contributions of the paper are threefold. First, the paper introduces a conceptual framework to guide the process...