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

Abstract

Over the past two decades, Germany experienced several periods of banking system instability rather than full-blown banking system crises. In this paper we introduce a continuous and forward-looking stability indicator for the banking system based on information on all financial institutions in Germany between 1995 and 2010. Explaining this measure by means of panel regression techniques, we identify significant macroprudential early warning indicators (such as asset price indicators, leading indicators for the business cycle and monetary indicators) and spillover effects. Whereas international spillovers play a significant role across all banking sectors, regional spillovers and the credit-to-GDP ratio are more important for cooperative banks and less relevant for commercial banks.Keywords: Early Warning Indicators, Banking System Stability, Regional Spillover Effects, Panel Regression Techniques.JEL classification: C23, E44, G01, G21. Non-technical SummaryRegular financial stability assessment and the identification of early warning indicators signaling emerging risks to the banking system are major tasks of central banks and supervisory authorities. The stability and efficiency of a banking system ensures the optimal allocation of capital resources in an economy, and regulators therefore aim to prevent banking system crises and their associated adverse feedback effects on the real economy. This paper introduces a continuous and forward-looking stability indicator for the German banking system which is used to identify early warning indicators and spillover effects in both regional banking and international financial markets.Over the past two decades, Germany experienced several periods of banking system instability rather than full-blown banking system crises. Instability could be observed across banking sectors either as a consequence of reforms in banking legislation or resulting from national and international developments in financial markets. To describe the condition of the banking system, we develop an indicator compiling a basket of banks containing both major financial institutions and smaller banks. The indicator comprises three components: an institution's score (i.e., the standardized probability of default), a credit spread and a stock market index for the banking sector. The probabilities of default are derived from the Bundesbank's hazard rate model for small banks; for large institutions, Moody's Bank Financial Strength Ratings (BFSR) are used. The empirical study is based on confidential supervisory reporting data provided by the Deutsche Bundesbank comprising up to 3,330 institutions over the period 1995 to 2010.Stability determinants of the national banking system can be classified into macroeconomic, financial and structural variables. Applying panel regression techniques, we find that asset price indicators, leading indicators for the business cycle and monetary indicators are reliable early warning indicators. In addition, international spillover effects play a significant role f...

In search of a measure of banking sector distress: empirical study of CESEE banking sectors

“…normalised variables similar to IMF financial soundness indicators, which use bank balance sheet data (see e.g Geršl and Heřmánek 2006;Jahn and Kick 2012;Ginevičius and Podviezko 2013;Petrovska and Mihajlovska 2013)…”

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“…The empirical analysis will focus on Germany, due to the size of this economy within the EU and the EMU. Furthermore, the country experienced banking system instabilities in previous periods (Jahn and Kick, 2012). Thus, the CISS (Composite Indicator of Systemic Stress) as the financial stress indicator should have captured these instabilities.…”

Financial stability of the banking sector in European countries: A comparative analysis

“…Van Roy (2003) determined that rigorous capital requirements in the early 1990's resulted in financial stability and reduced credit risk in the G10 countries. Jahn and Kick (2014), using the panel regression model, try to identify determinants of banking sector stability in Germany. The indicator consists of three parameters: probability of default of certain bank, credit spread and stock market index for the banking sector.…”