the web's biggest credit risk modeling resource.

Credit Jobs

Home Glossary Links FAQ / About Site Guide Search


Submit Your Paper

In Rememberance: World Trade Center (WTC)

doi> search: A or B

Export citation to:
- Text (plain)
- BibTeX

Bayesian Estimation of Probabilities of Default for Low Default Portfolios

by Dirk Tasche of Financial Services Authority, United Kingdom

April 5, 2012

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.

AMS Classification: 62P05.

Keywords: Low default portfolio, probability of default, upper confidence bound, Bayesian estimator.

Books Referenced in this paper:  (what is this?)

Download paper (552K PDF) 29 pages

Most Cited Books within Credit Scoring Papers