Estimating Probabilities of Default for German Savings Banks and Credit Cooperatives

Abstract

A healthy banking system is a fundamental condition for financial stability. When assessing the riskiness of the banking system, analysts often restrict their focus to large banks. This may create a distorted picture in countries like Germany with fragmented banking systems. In Germany, savings banks and cooperative banks taken together are important players in the market. However, little is known about their default risk. The reason is that these banks usually resolve financial distress within their own organisations, which means defaults are not observable from the outside. In this paper we use a new dataset which contains information about financial distress and financial strength of all German savings banks and cooperative banks. The data have been gathered by the Deutsche Bundesbank for microprudential supervision and have never before been exploited for macroprudential purposes. We use the data to identify the main risk drivers. To this end we estimate a default prediction model (hazard model). A second goal of the paper is to analyse the impact of macroeconomic information for forecasting banks' defaults. Recent findings for the USA have cast some doubt on the usefulness of macroeconomic information for banks' risk assessment. Contrary to recent literature, we find that macroeconomic information significantly improves default forecasts. Keywords:bank failure, default probability, time-discrete hazard rate Non-technical summaryThere is little evidence on the default risk of German savings banks and cooperative banks, although they constitute an integral part of the German banking system. Our aim is to develop a statistical system which estimates probabilities of default (PDs) for savings banks and cooperative banks. We also try to find evidence for the importance of macroeconomic developments for the estimation of PD. Since we adopt a prudential supervisory perspective, default is defined as any event that jeopardises the bank's viability as a going concern.The paper uses a dataset from the Deutsche Bundesbank that combines default events with balance sheet information, audit reports, and macroeconomic variables. The data is used to estimate a discrete hazard model. Hazard models are particularly suited for our purposes because they simultaneously include macroeconomic and microeconomic data.Our main findings are as follows.

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

“…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.…”

“…We therefore include the national private credit-to-GDP ratio in our analysis. 27 With respect to financial market indicators, we take into account the role of the interbank market, which has become especially important during the financial crisis of 2008/2009. To separate the effects of distress on the interbank market and monetary policy, we include the 3-month Libor over 3-month Bubill similar to a TED Spread for 25 Borio and Drehmann (2009) refer to financial imbalances as "growing fragility of private sector balance sheets during benign economic conditions", BIS Quarterly Review, March 2009, p. 30.…”

“…The authors argue that property prices have been more important in predicting banking crises than equity prices. 27 Concerning equity market indices, we do not include indicators such as the DAX 30/Euro Stoxx 50 Index or the Euro Stoxx Banks as stock market indicators for the European banking sector since we are interested in drivers of banking system stability apart from related stability indicator's individual components.…”

Do all new brooms sweep clean? Evidence for outside bank appointments

“…14 We wish to note that banks' real liquidity risk cannot be measured adequately with the data available at the Deutsche Bundesbank (Porath, 2006) and, in particular for small cooperative and savings banks, a high cash and interbank-loans to total assets ratio is rather an indicator of lacking business opportunities than low liquidity risk. Therefore, we follow Kick and Jahn (2014) and proxy banks' liquidity situation at an aggregate level instead by including the yield curve in the bank rating model.…”