To mitigate potential contagion from future banking crises, the European Commission recently proposed a framework which would provide for the bail-in of bank creditors in the event of failure. In this study, we examine this framework retrospectively in the context of failed European banks during the global financial crisis. Empirical findings suggest that equity and subordinated bond holders would have been the main losers from the €535 billion impairment losses realized by failed European banks. Losses attributed to senior debt holders would, on aggregate, have been proportionally small, while no losses would have been imposed on depositors. Cross-country analysis, incorporating stress-tests, reveals a divergence of outcomes with subordinated debt holders wiped out in a number of countries, while senior debt holders of Greek, Austrian and Irish banks would have required bail-in.

Banks must adhere to strict rules regarding the quantity of regulatory capital held but have some flexibility as to its composition. In this paper, we examine if bank insolvency (distance to default) is sensitive to capital other than common equity for a sample of listed North American and European banks. Decomposing tier 1 capital into tangible equity and non-core components reveals a series of heretofore unidentified non-linear links with insolvency risk. We assess the influence of binding capital requirements, finding that low regulatory capital buffers are associated with increased insolvency risk for banks holding greater quantities of non-core tier 1 and tier 2 capital. The links between insolvency and capital, evident when the latter is denominated relative to tangible assets or total regulatory capital, are found to be expunged when defined relative to risk-weighted assets.

Previous studies document statistically significant evidence of crude oil return predictability by several forecasting variables. We suggest that this evidence is misleading and follows from the common use of within-month averages of daily oil prices in calculating returns used in predictive regressions. Averaging introduces a bias in the estimates of the first-order autocorrelation coefficient and variance of returns. Consequently, estimates of regression coefficients are inefficient and associated t-statistics are overstated, leading to false inference about the true extent of in-sample and out-of-sample return predictability. On the contrary, using end-of-month data, we do not find convincing evidence for the predictability of oil returns. Our results highlight and provide a cautionary tale on how the choice of data could influence hypothesis testing for return predictability.