Slippery Slopes of Stress: Ordered Failure Events in German Banking

Abstract

Outright bank failures without prior indication of nancial instability are very rare. Supervisory authorities monitor banks constantly. Thus, they usually obtain early warning signals that precede ultimate failure and, in fact, banks can be regarded as troubled to varying degrees before outright closure. But to our knowledge virtually all studies that predict bank failures neglect the ordinal nature of bank distress. Exploiting the distress database of the Deutsche Bundesbank we distinguish four dierent distress events that banks experience. Only the worst entails a bank to exit the market. Weaker orders of distress are, rst, compulsory notications of the authorities about potential problems, second, corrective actions such as warnings and hearings and, third, actions by banking pillar's insurance schemes. Since the four categories of hazard functions are not proportional, we specify a generalized ordered logit model to estimate the respective probabilities of distress simultaneously. Our model estimates each set of probabilities with high accuracy and conrms, rst, the necessity to account for dierent kinds of distress events and, second, the violation of the proportional odds assumption implicit in most limited dependent analyses of bank failure.Keywords: Bank, failure, distress, generalized ordered logit JEL: C35, G21, G33, K23, L50 Non-technical summary Bank insolvencies are dierent from failures of non-nancial rms because they may disrupt trust in the banking system as a whole. This explains why banks are subject to more regulatory scrutiny compared to most other industries. Supervisors therefore monitor banks on an ongoing basis to ensure the nancial safety and soundness of individual banks and the system. To this end, they use among other things o-site monitoring systems, which rely mostly on bank-specic nancial data reported to the authorities. These so-called hazard rate models predict the probability of default of individual banks on the basis of historical nancial and default data.Obviously, the denition of default is crucial to such analyses. On the one hand, banks are subject to clear-cut failure denitions, such as minimum capital requirements stipulated in laws and regulations. On the other hand, the systemic dangers of bank defaults in conjunction with an ongoing supervision process suggest that regulators, and practitioners, may regard a bank as troubled much earlier than ultimate default in the form of (forced) market exit occurs. We argue in this paper that most bank hazard studies neglect this ordinal nature of what we prefer to dene as distress rather than default.We address the issue by using data on a number of distress events that German universal banks from all three pillars experienced between 1994 and 2004. We categorize these events into four groups of ascending degrees of distress, ranging from early warning information issued by banks to regulators to absorbing events such as closure and restructuring mergers. To account for this pecking order of events, we specify a genera...

Early Warning Indicators for the German Banking System: A Macroprudential Analysis

“…10 On the left-hand side of our logistic regression we use a unique data set of bank distress events collected by the Deutsche Bundesbank over the time period 1994 to 2006 which is only available for small banks. In contrast to previous studies (e.g., Porath (2004), Kick and Koetter (2007), etc.) this data set consists of a more detailed distress definition and also covers a longer time period (up to 2006) for which distress data is available.…”

Early Warning Indicators for the German Banking System: A Macroprudential Analysis

“…In contrast to previous studies (e.g., Porath (2004), Kick and Koetter (2007), etc.) this data set consists of a more detailed distress definition and also covers a longer time period (up to 2006) for which distress data is available.…”

“…Following Porath (2004) as well as Kick and Koetter (2007), we specify a bank rating model that is based on the logistic link function which transforms a set of bankspecific covariates and a financial variable observed in year 1 into the probability of default of that particular bank in year . The right-hand side of the regression equation is based on the CAMELS taxonomy: Capital adequacy, Asset quality, Management, Earnings, Liquidity, and Sensitivity to market risk.…”

Recovery Determinants of Distressed Banks: Regulators, Market Discipline, or the Environment?

“…Since this measure is highly correlated with commercial banks and because most banks are cooperatives in our sample, we are unable to estimate the model for banking groups separately or with banking group dummies and the incorporation indicator. 24 Choices are inspired by previous hazard rate studies of German banks (Kick and Koetter, 2007). Table 3 Summary statistics on regulatory, bank, and environmental covariates The Table shows descriptive statistics for 473 banks that received capital injections.…”