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Pitfalls and remedies in testing the calibration quality of rating systems

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  • Aussenegg, Wolfgang
  • Resch, Florian
  • Winkler, Gerhard

Abstract

Testing calibration quality by means of backtesting is an integral part in the validation of credit rating systems. Against this background this paper provides a comprehensive overview of existing testing procedures. We study the procedures' deficiencies theoretically and illustrate their impact empirically. Based on the insights gained therefrom, we develop enhanced hybrid testing procedures which turn out to be superior to the commonly applied methods. We also propose computationally efficient algorithms for our calibration tests. Finally, we are able to demonstrate empirically that our method outperforms existing tests in a scenario analysis using rating data of Moody's.

Suggested Citation

  • Aussenegg, Wolfgang & Resch, Florian & Winkler, Gerhard, 2011. "Pitfalls and remedies in testing the calibration quality of rating systems," Journal of Banking & Finance, Elsevier, vol. 35(3), pages 698-708, March.
    • Handle: RePEc:eee:jbfina:v:35:y:2011:i:3:p:698-708
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    References listed on IDEAS

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    1. Marwan Elkhoury, 2007. "Credit Rating Agencies And Their Potential Impact On Developing Countries," UNCTAD Discussion Papers 186, United Nations Conference on Trade and Development.
    2. Krahnen, Jan Pieter & Weber, Martin, 2001. "Generally accepted rating principles: A primer," Journal of Banking & Finance, Elsevier, vol. 25(1), pages 3-23, January.
    3. Khandani, Amir E. & Kim, Adlar J. & Lo, Andrew W., 2010. "Consumer credit-risk models via machine-learning algorithms," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2767-2787, November.
    4. Medema, Lydian & Koning, Ruud H. & Lensink, Robert, 2009. "A practical approach to validating a PD model," Journal of Banking & Finance, Elsevier, vol. 33(4), pages 701-708, April.
    5. Kerkhof, Jeroen & Melenberg, Bertrand & Schumacher, Hans, 2010. "Model risk and capital reserves," Journal of Banking & Finance, Elsevier, vol. 34(1), pages 267-279, January.
    6. Tarashev, Nikola, 2010. "Measuring portfolio credit risk correctly: Why parameter uncertainty matters," Journal of Banking & Finance, Elsevier, vol. 34(9), pages 2065-2076, September.
    7. Rosen, Dan & Saunders, David, 2010. "Risk factor contributions in portfolio credit risk models," Journal of Banking & Finance, Elsevier, vol. 34(2), pages 336-349, February.
    8. Lando, David & Skodeberg, Torben M., 2002. "Analyzing rating transitions and rating drift with continuous observations," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 423-444, March.
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    Cited by:

    1. Wosnitza, Jan Henrik, 2022. "Calibration alternatives to logistic regression and their potential for transferring the dispersion of discriminatory power into uncertainties of probabilities of default," Discussion Papers 04/2022, Deutsche Bundesbank.
    2. António Antunes & Homero Gonçalves & Pedro Prego, 2017. "Firm default probabilities revisited," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Uses of central balance sheet data offices' information, volume 45, Bank for International Settlements.
    3. Sauer, Stephan & Coppens, François & Mayer, Manuel & Millischer, Laurent & Resch, Florian & Schulze, Klaas, 2016. "Advances in multivariate back-testing for credit risk underestimation," Working Paper Series 1885, European Central Bank.

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