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Measuring portfolio credit risk correctly: Why parameter uncertainty matters

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  • Tarashev, Nikola
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    Abstract

    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.

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    File URL: http://www.sciencedirect.com/science/article/B6VCY-4Y95RKR-4/2/49f01ba1d68420b1b737e7ca3d0c8e2e
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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Banking & Finance.

    Volume (Year): 34 (2010)
    Issue (Month): 9 (September)
    Pages: 2065-2076
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    Handle: RePEc:eee:jbfina:v:34:y:2010:i:9:p:2065-2076

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    Web page: http://www.elsevier.com/locate/jbf

    For corrections or technical questions regarding this item, or to correct its listing, contact: (Jeroen Loos).

    Related research

    Keywords: Correlated defaults Estimation error Risk management;

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