Bank Risk Proxies and the Crisis of 2007/09: a Comparison
Applied Economics Letters,
The global financial crisis has again shown that it is important to understand the emergence and measurement of risks in the banking sector. However, there is no consensus in the literature which risk proxy works best at the level of the individual bank. A commonly used measure in applied work is the Z-score, which might suffer from calculation issues given poor data quality. Motivated by the variety of bank risk proxies, our analysis reveals that nonperforming assets are a well-suited complement to the Z-score in studies of bank risk.
Stabile Finanzmärkte: Was uns die Forschung lehrt Dossier ...
Bank Risk Proxies and the Crisis of 2007/09: A Comparison
IWH Discussion Papers,
Motivated by the variety of bank risk proxies, our analysis reveals that nonperforming assets are a well-suited complement to the Z-score in studies of bank risk.
We investigate four proxies for bank risk that are frequently used in the literature. Our analysis shows that non-performing assets are a good proxy for bank risk for two reasons. First, non-performing assets nest the alternative proxies as shown by the high share of variation in non-performing assets explained by the Z-score, loan loss reserves and loan loss provisions. Second, non-performing assets are well-suited to explain bank failures one year ahead. The latter point also holds for the Z-score whereby the information content of the Z-score seems to differ from the other variables. We conclude that non-performing assets are a well-suited complement to the Z-score, which may come with calculation issues regarding the volatility of profitability, in studies of bank risk.
The Stability of Bank Efficiency Rankings when Risk Preferences and Objectives are Different
The European Journal of Finance,
We analyze the stability of efficiency rankings of German universal banks between 1993 and 2004. First, we estimate traditional efficiency scores with stochastic cost and alternative profit frontier analysis. Then, we explicitly allow for different risk preferences and measure efficiency with a structural model based on utility maximization. Using the almost ideal demand system, we estimate input- and profit-demand functions to obtain proxies for expected return and risk. Efficiency is then measured in this risk-return space. Mean risk-return efficiency is somewhat higher than cost and considerably higher than profit efficiency (PE). More importantly, rank–order correlation between these measures are low or even negative. This suggests that best-practice institutes should not be identified on the basis of traditional efficiency measures alone. Apparently, low cost and/or PE may merely result from alternative yet efficiently chosen risk-return trade-offs.