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Measuring portfolio credit risk correctly: why parameter uncertainty matters

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  • Nikola Tarashev

Abstract

Why 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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  • Nikola Tarashev, 2009. "Measuring portfolio credit risk correctly: why parameter uncertainty matters," BIS Working Papers 280, Bank for International Settlements.
  • Handle: RePEc:bis:biswps:280
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    12. Nikola Tarashev & Haibin Zhu, 2008. "Specification and Calibration Errors in Measures of Portfolio Credit Risk: The Case of the ASRF Model," International Journal of Central Banking, International Journal of Central Banking, vol. 4(2), pages 129-173, June.
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    Cited by:

    1. Henry Penikas, 2023. "Unaccounted model risk for Basel IRB models deemed acceptable by conventional validation criteria," Risk Management, Palgrave Macmillan, vol. 25(4), pages 1-25, December.
    2. Radu Tunaru, 2015. "Model Risk in Financial Markets:From Financial Engineering to Risk Management," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 9524.
    3. Baviera, Roberto, 2022. "The measure of model risk in credit capital requirements," Finance Research Letters, Elsevier, vol. 44(C).
    4. Roberto Violi, 2010. "Credit ratings in structured finance and the role of systemic risk," Temi di discussione (Economic working papers) 774, Bank of Italy, Economic Research and International Relations Area.
    5. Chollete, Lorán & de la Peña, Victor & Lu, Ching-Chih, 2012. "International diversification: An extreme value approach," Journal of Banking & Finance, Elsevier, vol. 36(3), pages 871-885.
    6. Valerio Vacca, 2011. "An unexpected crisis? Looking at pricing effectiveness of different banks," Temi di discussione (Economic working papers) 814, Bank of Italy, Economic Research and International Relations Area.
    7. Nikola Tarashev & Claudio Borio & Kostas Tsatsaronis, 2009. "The systemic importance of financial institutions," BIS Quarterly Review, Bank for International Settlements, September.
    8. Goel, Tirupam & Lewrick, Ulf & Tarashev, Nikola, 2020. "Bank capital allocation under multiple constraints," Journal of Financial Intermediation, Elsevier, vol. 44(C).
    9. Adonis Antoniades & Nikola Tarashev, 2014. "Securitisations: tranching concentrates uncertainty," BIS Quarterly Review, Bank for International Settlements, December.
    10. Roberto Baviera, 2020. "The measure of model risk in credit capital requirements," Papers 2010.08028, arXiv.org.
    11. Valeriane Jokhadze & Wolfgang M. Schmidt, 2020. "Measuring Model Risk In Financial Risk Management And Pricing," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 23(02), pages 1-37, April.
    12. Claußen, Arndt & Rösch, Daniel & Schmelzle, Martin, 2019. "Hedging parameter risk," Journal of Banking & Finance, Elsevier, vol. 100(C), pages 111-121.
    13. Pflug, Georg Ch. & Pichler, Alois & Wozabal, David, 2012. "The 1/N investment strategy is optimal under high model ambiguity," Journal of Banking & Finance, Elsevier, vol. 36(2), pages 410-417.
    14. Henry Penikas, 2022. "Model Risk for Acceptable, but Imperfect, Discrimination and Calibration in Basel PD and LGD Models," Bank of Russia Working Paper Series wps92, Bank of Russia.
    15. Tunaru, Radu & Zheng, Teng, 2017. "Parameter estimation risk in asset pricing and risk management: A Bayesian approach," International Review of Financial Analysis, Elsevier, vol. 53(C), pages 80-93.
    16. Andrés Alonso Robisco & José Manuel Carbó Martínez, 2022. "Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-35, December.
    17. Chollete, Lorán & de la Peña, Victor & Lu, Ching-Chih, 2011. "International diversification: A copula approach," Journal of Banking & Finance, Elsevier, vol. 35(2), pages 403-417, February.
    18. Dannenberg, Henry, 2011. "The Importance of Estimation Uncertainty in a Multi-Rating Class Loan Portfolio," IWH Discussion Papers 11/2011, Halle Institute for Economic Research (IWH).
    19. Aussenegg, Wolfgang & Resch, Florian & Winkler, Gerhard, 2011. "Pitfalls and remedies in testing the calibration quality of rating systems," Journal of Banking & Finance, Elsevier, vol. 35(3), pages 698-708, March.

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    Keywords

    correlated defaults; estimation error; risk management;
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