Measuring portfolio credit risk correctly: Why parameter uncertainty matters
AbstractWhy should risk management systems account for parameter uncertainty? In addressing this question, the paper lets an investor in a credit portfolio face non-diversifiable uncertainty about two risk parameters - probability of default and asset-return correlation - and calibrates this uncertainty to a lower bound on estimation noise. In this context, a Bayesian inference procedure is essential for deriving and analyzing the main result, i.e. that parameter uncertainty raises substantially the tail risk perceived by the investor. Since a measure of tail risk that incorporates parameter uncertainty is computationally demanding, the paper also derives a closed-form approximation to such a measure.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Banking & Finance.
Volume (Year): 34 (2010)
Issue (Month): 9 (September)
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Web page: http://www.elsevier.com/locate/jbf
Correlated defaults Estimation error Risk management;
Other versions of this item:
- Nikola Tarashev, 2009. "Measuring portfolio credit risk correctly: why parameter uncertainty matters," BIS Working Papers 280, Bank for International Settlements.
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