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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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Bibliographic Info

Paper provided by Bank for International Settlements in its series BIS Working Papers with number 280.

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Length: 43 pages
Date of creation: Apr 2009
Date of revision:
Handle: RePEc:bis:biswps:280

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Keywords: correlated defaults; estimation error; risk management;

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References

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  1. Michael B. Gordy, 2002. "A risk-factor model foundation for ratings-based bank capital rules," Finance and Economics Discussion Series 2002-55, Board of Governors of the Federal Reserve System (U.S.).
  2. 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.
  3. Zhu, Haibin & Tarashev, Nikola A., 2008. "The pricing of correlated default risk: evidence from the credit derivatives market," Discussion Paper Series 2: Banking and Financial Studies 2008,09, Deutsche Bundesbank, Research Centre.
  4. Bongaerts, Dion & Charlier, Erwin, 2009. "Private equity and regulatory capital," Journal of Banking & Finance, Elsevier, vol. 33(7), pages 1211-1220, July.
  5. Merton, Robert C., 1973. "On the pricing of corporate debt: the risk structure of interest rates," Working papers 684-73., Massachusetts Institute of Technology (MIT), Sloan School of Management.
  6. Lando, David & Skodeberg, Torben M., 2002. "Analyzing rating transitions and rating drift with continuous observations," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 423-444, March.
  7. Kerkhof, Jeroen & Melenberg, Bertrand & Schumacher, Hans, 2010. "Model risk and capital reserves," Journal of Banking & Finance, Elsevier, vol. 34(1), pages 267-279, January.
  8. Hanson, Samuel & Schuermann, Til, 2006. "Confidence intervals for probabilities of default," Journal of Banking & Finance, Elsevier, vol. 30(8), pages 2281-2301, August.
  9. Forte, Santiago & Peña, Juan Ignacio, 2009. "Credit spreads: An empirical analysis on the informational content of stocks, bonds, and CDS," Journal of Banking & Finance, Elsevier, vol. 33(11), pages 2013-2025, November.
  10. Lütkebohmert, Eva & Gordy, Michael B., 2007. "Granularity adjustment for Basel II," Discussion Paper Series 2: Banking and Financial Studies 2007,01, Deutsche Bundesbank, Research Centre.
  11. Liao, Hsien-Hsing & Chen, Tsung-Kang & Lu, Chia-Wu, 2009. "Bank credit risk and structural credit models: Agency and information asymmetry perspectives," Journal of Banking & Finance, Elsevier, vol. 33(8), pages 1520-1530, August.
  12. Borio, Claudio & Tsatsaronis, Kostas, 2004. "Accounting and prudential regulation: from uncomfortable bedfellows to perfect partners?," Journal of Financial Stability, Elsevier, vol. 1(1), pages 111-135, September.
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Cited by:
  1. 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.
  2. Henry Dannenberg, 2011. "The Importance of Estimation Uncertainty in a Multi-Rating Class Loan Portfolio," IWH Discussion Papers 11, Halle Institute for Economic Research.
  3. 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.
  4. 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.
  5. 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.

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