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Crash Testing German Banks

Author

Listed:
  • Klaus Duellmann

    (Deutsche Bundesbank)

  • Martin Erdelmeier

    (Deutsche Bundesbank)

Abstract

In this paper we stress-test credit portfolios of twenty-eight German banks based on a Merton-type multifactor credit-risk model. The stress scenario is an economic downturn in the automobile sector. Although the percentage of loans in the automobile sector is relatively low for all banks in the sample, the expected loss conditional on the stress event increases substantially by 70–80 percent for the total portfolio. This result confirms the need to account for hidden sectoral concentration risk because the increase in expected loss is driven mainly by correlation effects with related industry sectors. Therefore, credit-risk dependencies between sectors have to be adequately captured even if the trigger event is confined to a single sector. Finally, we calculate the impact on banks’ own-funds ratios, which decrease on average from 12 percent to 11.4 percent due to the stress event, which indicates that banks overall remain well capitalized. These main results are robust against various robustness checks, namely those concerning the granularity of the credit portfolio, the level of intersector asset correlations, and a cross-sectional variation of intrasector asset correlations.

Suggested Citation

  • 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.
  • Handle: RePEc:ijc:ijcjou:y:2009:q:3:a:5
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    References listed on IDEAS

    as
    1. Hanson, S. & Pesaran, M.H. & Schuermann, T., 2005. "Scope for Credit Risk Diversification," Cambridge Working Papers in Economics 0519, Faculty of Economics, University of Cambridge.
    2. Lütkebohmert, Eva & Gordy, Michael B., 2007. "Granularity adjustment for Basel II," Discussion Paper Series 2: Banking and Financial Studies 2007,01, Deutsche Bundesbank.
    3. Düllmann, Klaus & Scheicher, Martin & Schmieder, Christian, 2007. "Asset correlations and credit portfolio risk: an empirical analysis," Discussion Paper Series 2: Banking and Financial Studies 2007,13, Deutsche Bundesbank.
    4. 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.
    5. Gordy, Michael B., 2003. "A risk-factor model foundation for ratings-based bank capital rules," Journal of Financial Intermediation, Elsevier, vol. 12(3), pages 199-232, July.
    6. 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.
    7. 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.
    8. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    9. Ang, Andrew & Chen, Joseph, 2002. "Asymmetric correlations of equity portfolios," Journal of Financial Economics, Elsevier, vol. 63(3), pages 443-494, March.
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    Citations

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    Cited by:

    1. Packham, Natalie & Kalkbrener, Michael & Overbeck, Ludger, 2014. "Default probabilities and default correlations under stress," Frankfurt School - Working Paper Series 211, Frankfurt School of Finance and Management.
    2. 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.
    3. 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.
    4. Borio, Claudio & Drehmann, Mathias & Tsatsaronis, Kostas, 2014. "Stress-testing macro stress testing: Does it live up to expectations?," Journal of Financial Stability, Elsevier, vol. 12(C), pages 3-15.
    5. Duellmann, Klaus & Kick, Thomas, 2012. "Stress testing German banks against a global cost-of-capital shock," Discussion Papers 04/2012, Deutsche Bundesbank.
    6. Vazquez, Francisco & Tabak, Benjamin M. & Souto, Marcos, 2012. "A macro stress test model of credit risk for the Brazilian banking sector," Journal of Financial Stability, Elsevier, vol. 8(2), pages 69-83.
    7. Wosnitza, Jan Henrik & Leker, Jens, 2014. "Why credit risk markets are predestined for exhibiting log-periodic power law structures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 427-449.
    8. Gross, Marco & Población García, Francisco Javier, 2015. "A false sense of security in applying handpicked equations for stress test purposes," Working Paper Series 1845, European Central Bank.
    9. Natalia Podlich & Didar Illyasov & Elena Tsoy & Shynar Shaikh, 2010. "The Methodology of Stress Tests for the Kazakh Banking System," ifo Working Paper Series 85, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.

    More about this item

    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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