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Asset correlations and credit portfolio risk: an empirical analysis

Author

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  • Düllmann, Klaus
  • Scheicher, Martin
  • Schmieder, Christian

Abstract

In credit risk modelling, the correlation of unobservable asset returns is a crucial component for the measurement of portfolio risk. In this paper, we estimate asset correlations from monthly time series of Moody's KMV asset values for around 2,000 European firms from 1996 to 2004. We compare correlation and value-atrisk (VaR) estimates in a one-factor or market model and a multi-factor or sector model. Our main finding is a complex interaction of credit risk correlations and default probabilities affecting total credit portfolio risk. Differentiation between industry sectors when using the sector model instead of the market model has only a secondary effect on credit portfolio risk, at least for the underlying credit portfolio. Averaging firm-dependent asset correlations on a sector level can, however, cause a substantial underestimation of the VaR in a portfolio with heterogeneous borrower size. This result holds for the market as well as the sector model. Furthermore, the VaR of the IRB model is more stable over time than the VaR of the market model and the sector model, while its distance from the other two models fluctuates over time.

Suggested Citation

  • 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.
  • Handle: RePEc:zbw:bubdp2:6352
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    Citations

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    Cited by:

    1. Klaus Duellmann & Jonathan Küll & Michael Kunisch, 2010. "Estimating asset correlations from stock prices or default rates - which method is superior?," Post-Print hal-00736734, HAL.
    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. repec:eee:glofin:v:34:y:2017:i:c:p:89-99 is not listed on IDEAS
    4. Simone Varotto, 2008. "An Assessment of the Internal Rating Based Approach in Basel II," ICMA Centre Discussion Papers in Finance icma-dp2008-04, Henley Business School, Reading University.
    5. Düllmann, Klaus & Kunisch, Michael & Küll, Jonathan, 2008. "Estimating asset correlations from stock prices or default rates: which method is superior?," Discussion Paper Series 2: Banking and Financial Studies 2008,04, Deutsche Bundesbank.
    6. Duellmann, Klaus & Küll, Jonathan & Kunisch, Michael, 2010. "Estimating asset correlations from stock prices or default rates--Which method is superior?," Journal of Economic Dynamics and Control, Elsevier, vol. 34(11), pages 2341-2357, November.
    7. Schmidt, Rafael & Schmieder, Christian, 2009. "Modelling dynamic portfolio risk using risk drivers of elliptical processes," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 229-244, April.
    8. Daniel C Hardy & Christian Schmieder, 2013. "Rules of Thumb for Bank Solvency Stress Testing," IMF Working Papers 13/232, International Monetary Fund.
    9. Nikola Tarashev & Haibin Zhu, 2007. "Measuring portfolio credit risk: modelling versus calibration errors," BIS Quarterly Review, Bank for International Settlements, March.
    10. Nikola Tarashev & Haibin Zhu, 2008. "Specification and Calibration Errors in Measures of Portfolio Credit Risk: The Case of the ASRF Model," International Journal of Central Banking, International Journal of Central Banking, vol. 4(2), pages 129-173, June.
    11. Mager, Ferdinand & Schmieder, Christian, 2008. "Stress testing of real credit portfolios," Discussion Paper Series 2: Banking and Financial Studies 2008,17, Deutsche Bundesbank.
    12. Nikola A. Tarashev & Haibin Zhu, 2007. "Modelling and calibration errors in measures of portfolio credit risk," BIS Working Papers 230, Bank for International Settlements.

    More about this item

    Keywords

    Asset correlations; sector concentration; credit portfolio risk;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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