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Modelling and calibration errors in measures of portfolio credit risk

Author

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  • Nikola A. Tarashev
  • Haibin Zhu

Abstract

This paper develops an empirical procedure for analyzing the impact of model misspecification and calibration errors on measures of portfolio credit risk. When applied to large simulated portfolios with realistic characteristics, this procedure reveals that violations of key assumptions of the well-known Asymptotic Single-Risk Factor (ASRF) model are virtually inconsequential. By contrast, flaws in the calibrated interdependence of credit risk across exposures, which are driven by plausible small-sample estimation errors or popular rule-of-thumb values of asset return correlations, can lead to significant inaccuracies in measures of portfolio credit risk. Similar inaccuracies arise under erroneous, albeit standard, assumptions regarding the tails of the distribution of asset returns.

Suggested Citation

  • Nikola A. Tarashev & Haibin Zhu, 2007. "Modelling and calibration errors in measures of portfolio credit risk," BIS Working Papers 230, Bank for International Settlements.
  • Handle: RePEc:bis:biswps:230
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    References listed on IDEAS

    as
    1. Lütkebohmert, Eva & Gordy, Michael B., 2007. "Granularity adjustment for Basel II," Discussion Paper Series 2: Banking and Financial Studies 2007,01, Deutsche Bundesbank.
    2. 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.
    3. Susanne Emmer & Dirk Tasche, 2003. "Calculating credit risk capital charges with the one-factor model," Papers cond-mat/0302402, arXiv.org, revised Jan 2005.
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    Citations

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    Cited by:

    1. Christoph Wunderer, 2017. "Asset correlation estimation for inhomogeneous exposure pools," Papers 1701.02028, arXiv.org.
    2. William R. White, 2007. "The housing finance revolution: commentary," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 69-84.
    3. Marcin Łupiński, 2013. "Statistical Data and Models Used for Analysis and Management of Financial Stability at the Macro Level," Ekonomia journal, Faculty of Economic Sciences, University of Warsaw, vol. 32.
    4. M. Dietsch & K. Düllmann & H. Fraisse & P. Koziol & C. Ott, 2016. "Support for the SME Supporting Factor - Multi-country empirical evidence on systematic risk factor for SME loans," Débats économiques et financiers 23, Banque de France.
    5. Antão, Paula & Lacerda, Ana, 2011. "Capital requirements under the credit risk-based framework," Journal of Banking & Finance, Elsevier, vol. 35(6), pages 1380-1390, June.
    6. Jaromir Benes & Michael Kumhof & Douglas Laxton, 2014. "Financial Crises in DSGE Models; A Prototype Model," IMF Working Papers 14/57, International Monetary Fund.

    More about this item

    Keywords

    Correlated defaults; value at risk; multiple common factors; granularity; estimation error; tail dependence; bank capital;

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