Measuring portfolio credit risk correctly: Why parameter uncertainty matters
Why 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.
Year of publication: |
2010
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Authors: | Tarashev, Nikola |
Published in: |
Journal of Banking & Finance. - Elsevier, ISSN 0378-4266. - Vol. 34.2010, 9, p. 2065-2076
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Publisher: |
Elsevier |
Keywords: | Correlated defaults Estimation error Risk management |
Saved in:
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