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Bayesian estimation of probabilities of default for low default portfolios

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  • Dirk Tasche

Abstract

The estimation of probabilities of default (PDs) for low default portfolios by means of upper confidence bounds is a well established procedure in many financial institutions. However, there are often discussions within the institutions or between institutions and supervisors about which confidence level to use for the estimation. The Bayesian estimator for the PD based on the uninformed, uniform prior distribution is an obvious alternative that avoids the choice of a confidence level. In this paper, we demonstrate that in the case of independent default events the upper confidence bounds can be represented as quantiles of a Bayesian posterior distribution based on a prior that is slightly more conservative than the uninformed prior. We then describe how to implement the uninformed and conservative Bayesian estimators in the dependent one- and multi-period default data cases and compare their estimates to the upper confidence bound estimates. The comparison leads us to suggest a constrained version of the uninformed (neutral) Bayesian estimator as an alternative to the upper confidence bound estimators.

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File URL: http://arxiv.org/pdf/1112.5550
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Paper provided by arXiv.org in its series Papers with number 1112.5550.

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Date of creation: Dec 2011
Date of revision: Aug 2013
Publication status: Published in Journal of Risk Management in Financial Institutions 6 (3), 302-326, 2013
Handle: RePEc:arx:papers:1112.5550

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  1. Katja Pluto & Dirk Tasche, 2004. "Estimating Probabilities of Default for Low Default Portfolios," Papers cond-mat/0411699, arXiv.org, revised Apr 2005.
  2. Kiefer, Nicholas M., 2009. "Default estimation for low-default portfolios," Journal of Empirical Finance, Elsevier, Elsevier, vol. 16(1), pages 164-173, January.
  3. Alexander Shapiro & Jos Berge, 2002. "Statistical inference of minimum rank factor analysis," Psychometrika, Springer, vol. 67(1), pages 79-94, March.
  4. Dirk Tasche, 2002. "Expected Shortfall and Beyond," Papers cond-mat/0203558, arXiv.org, revised Oct 2002.
  5. Kiefer, Nicholas M., 2008. "Default Estimation, Correlated Defaults, and Expert Information," Working Papers 08-02, Cornell University, Center for Analytic Economics.
  6. Acerbi, Carlo, 2002. "Spectral measures of risk: A coherent representation of subjective risk aversion," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1505-1518, July.
  7. Kiefer, Nicholas M., 2010. "Default Estimation and Expert Information," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 28(2), pages 320-328.
  8. Dirk Tasche, 2009. "Estimating discriminatory power and PD curves when the number of defaults is small," Papers 0905.3928, arXiv.org, revised Mar 2010.
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