Measuring portfolio credit risk correctly: why parameter uncertainty matters
AbstractWhy should risk management systems account for parameter uncertainty? In order to answer this question, this paper lets an investor in a credit portfolio face non-diversifiable estimation-driven uncertainty about two parameters: probability of default and asset-return correlation. Bayesian inference reveals that - for realistic assumptions about the portfolio's credit quality and the data underlying parameter estimates - this uncertainty substantially increases the tail risk perceived by the investor. Since incorporating parameter uncertainty in a measure of tail risk is computationally demanding, the paper also derives and analyzes a closed-form approximation to such a measure.
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Bibliographic InfoPaper provided by Bank for International Settlements in its series BIS Working Papers with number 280.
Length: 43 pages
Date of creation: Apr 2009
Date of revision:
correlated defaults; estimation error; risk management;
Other versions of this item:
- Tarashev, Nikola, 2010. "Measuring portfolio credit risk correctly: Why parameter uncertainty matters," Journal of Banking & Finance, Elsevier, vol. 34(9), pages 2065-2076, September.
- NEP-ALL-2009-04-25 (All new papers)
- NEP-BAN-2009-04-25 (Banking)
- NEP-RMG-2009-04-25 (Risk Management)
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