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Stress testing German banks against a global credit crunch

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  • Klaus Düllmann
  • Thomas Kick

Abstract

This paper investigates the impact of a global credit crunch on the corporate credit portfolios of large German banks using a two-stage approach. First, a macroeconometric simulation model (NiGEM) is used to forecast the impact of a substantial increase in the cost of business capital for firms worldwide in three particularly export-oriented industry sectors in Germany. Second, the impact of this economic multi-sector stress on bank credit portfolios is captured by a state-of-the-art Credit Metrics-type portfolio model with sector-dependent unobservable risk factors as drivers of the systematic risk. In our assessment of capital ratios, we confirm that both the increase of the capital charge for the unexpected loss and the increase in banks’ expected losses need to be considered. We also find that the availability of granular information at the level of borrower-specific probabilities of default has a significant impact on the stress test results. Copyright Swiss Society for Financial Market Research 2014

Suggested Citation

  • Klaus Düllmann & Thomas Kick, 2014. "Stress testing German banks against a global credit crunch," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 28(4), pages 337-361, November.
    • Handle: RePEc:kap:fmktpm:v:28:y:2014:i:4:p:337-361
    DOI: 10.1007/s11408-014-0236-y
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    References listed on IDEAS

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    1. Klaus Düllmann & Nancy Masschelein, 2007. "A Tractable Model to Measure Sector Concentration Risk in Credit Portfolios," Journal of Financial Services Research, Springer;Western Finance Association, vol. 32(1), pages 55-79, October.
    2. Klaus Duellmann & Martin Erdelmeier, 2009. "Crash Testing German Banks," International Journal of Central Banking, International Journal of Central Banking, vol. 5(3), pages 139-175, September.
    3. Helmut Elsinger & Alfred Lehar & Martin Summer, 2006. "Using Market Information for Banking System Risk Assessment," International Journal of Central Banking, International Journal of Central Banking, vol. 2(1), March.
    4. Hanson, Samuel G. & Pesaran, M. Hashem & Schuermann, Til, 2008. "Firm heterogeneity and credit risk diversification," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 583-612, September.
    5. Lutz Hahnenstein, 2004. "Calibrating the CreditMetrics™ correlation concept — Empirical evidence from Germany," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 18(4), pages 358-381, December.
    6. Thomas Breuer & Martin Jandacka & Klaus Rheinberger & Martin Summer, 2009. "How to Find Plausible, Severe and Useful Stress Scenarios," International Journal of Central Banking, International Journal of Central Banking, vol. 5(3), pages 205-224, September.
    7. Miguel A. Segoviano, 2006. "Portfolio Credit Risk and Macroeconomic Shocks: Applications to Stress Testing Under Data-Restricted Environments," IMF Working Papers 2006/283, International Monetary Fund.
    8. De Graeve, F. & Kick, T. & Koetter, M., 2008. "Monetary policy and financial (in)stability: An integrated micro-macro approach," Journal of Financial Stability, Elsevier, vol. 4(3), pages 205-231, September.
    9. Mojon, Benoît & Peersman, Gert, 2001. "A VAR description of the effects of monetary policy in the individual countries of the euro area," Working Paper Series 92, European Central Bank.
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    Citations

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    Cited by:

    1. Koziol, Philipp & Schell, Carmen & Eckhardt, Meik, 2015. "Credit risk stress testing and copulas: Is the Gaussian copula better than its reputation?," Discussion Papers 46/2015, Deutsche Bundesbank.
    2. Siemsen, Thomas & Vilsmeier, Johannes, 2017. "A stress test framework for the German residential mortgage market: Methodology and application," Discussion Papers 37/2017, Deutsche Bundesbank.
    3. Tente, Natalia & von Westernhagen, Natalja & Slopek, Ulf, 2017. "M-PRESS-CreditRisk: A holistic micro- and macroprudential approach to capital requirements," Discussion Papers 15/2017, Deutsche Bundesbank.
    4. Pliszka, Kamil, 2021. "System-wide and banks' internal stress tests: Regulatory requirements and literature review," Discussion Papers 19/2021, Deutsche Bundesbank.
    5. Busch, Ramona & Koziol, Philipp & Mitrovic, Marc, 2015. "Many a little makes a mickle: Macro portfolio stress test for small and medium-sized German banks," Discussion Papers 23/2015, Deutsche Bundesbank.
    6. Natalia Tente & Natalja Von Westernhagen & Ulf Slopek, 2019. "M‐PRESS‐CreditRisk: Microprudential and Macroprudential Capital Requirements for Credit Risk under Systemic Stress," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 51(7), pages 1923-1961, October.
    7. Ferrari, Stijn & Van Roy, Patrick & Vespro, Cristina, 2021. "Sensitivity of credit risk stress test results: Modelling issues with an application to Belgium," Journal of Financial Stability, Elsevier, vol. 52(C).
    8. Busch, Ramona & Koziol, Philipp & Mitrovic, Marc, 2018. "Many a little makes a mickle: Stress testing small and medium-sized German banks," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 237-253.

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    More about this item

    Keywords

    Asset correlation; Portfolio credit risk; Macroeconomic stress tests; G21; G33; C13; C15;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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