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A coupled Markov chain approach to credit risk modeling

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  • Wozabal, David
  • Hochreiter, Ronald

Abstract

We propose a Markov chain model for credit rating changes. We do not use any distributional assumptions on the asset values of the rated companies but directly model the rating transitions process. The parameters of the model are estimated by a maximum likelihood approach using historical rating transitions and heuristic global optimization techniques.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

Volume (Year): 36 (2012)
Issue (Month): 3 ()
Pages: 403-415

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Handle: RePEc:eee:dyncon:v:36:y:2012:i:3:p:403-415

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

Related research

Keywords: Credit risk; Markov models; Ratings; Conditional value-at-risk; Bond portfolios;

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  1. Korolkiewicz, Malgorzata W. & Elliott, Robert J., 2008. "A hidden Markov model of credit quality," Journal of Economic Dynamics and Control, Elsevier, vol. 32(12), pages 3807-3819, December.
  2. Renault, Olivier & Scaillet, Olivier, 2004. "On the way to recovery: A nonparametric bias free estimation of recovery rate densities," Journal of Banking & Finance, Elsevier, vol. 28(12), pages 2915-2931, December.
  3. Michael B. Gordy, 1998. "A comparative anatomy of credit risk models," Finance and Economics Discussion Series 1998-47, Board of Governors of the Federal Reserve System (U.S.).
  4. Stefanescu, Catalina & Tunaru, Radu & Turnbull, Stuart, 2009. "The credit rating process and estimation of transition probabilities: A Bayesian approach," Journal of Empirical Finance, Elsevier, vol. 16(2), pages 216-234, March.
  5. Shirley J. Huang & Jun Yu, 2009. "Bayesian Analysis of Structural Credit Risk Models with Microstructure Noises," Finance Working Papers 23054, East Asian Bureau of Economic Research.
  6. Kiefer, Nicholas M. & Larson, C. Erik, 2007. "A simulation estimator for testing the time homogeneity of credit rating transitions," Journal of Empirical Finance, Elsevier, vol. 14(5), pages 818-835, December.
  7. Masaaki Kijima, 1998. "Monotonicities in a Markov Chain Model for Valuing Corporate Bonds Subject to Credit Risk," Mathematical Finance, Wiley Blackwell, vol. 8(3), pages 229-247.
  8. Giesecke, Kay & Weber, Stefan, 2006. "Credit contagion and aggregate losses," Journal of Economic Dynamics and Control, Elsevier, vol. 30(5), pages 741-767, May.
  9. Crouhy, Michel & Galai, Dan & Mark, Robert, 2000. "A comparative analysis of current credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 59-117, January.
  10. Kaniovski, Y.M. & Pflug, G.Ch., 2007. "Risk assessment for credit portfolios: A coupled Markov chain model," Journal of Banking & Finance, Elsevier, vol. 31(8), pages 2303-2323, August.
  11. Altman, Edward I., 1998. "The importance and subtlety of credit rating migration," Journal of Banking & Finance, Elsevier, vol. 22(10-11), pages 1231-1247, October.
  12. Frydman, Halina & Schuermann, Til, 2008. "Credit rating dynamics and Markov mixture models," Journal of Banking & Finance, Elsevier, vol. 32(6), pages 1062-1075, June.
  13. McNeil, Alexander J. & Wendin, Jonathan P., 2007. "Bayesian inference for generalized linear mixed models of portfolio credit risk," Journal of Empirical Finance, Elsevier, vol. 14(2), pages 131-149, March.
  14. Nickell, Pamela & Perraudin, William & Varotto, Simone, 2000. "Stability of rating transitions," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 203-227, January.
  15. 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.
  16. Jarrow, Robert A & Lando, David & Turnbull, Stuart M, 1997. "A Markov Model for the Term Structure of Credit Risk Spreads," Review of Financial Studies, Society for Financial Studies, vol. 10(2), pages 481-523.
  17. Stefan Weber & Kay Giesecke, 2003. "Credit Contagion and Aggregate Losses," Computing in Economics and Finance 2003 246, Society for Computational Economics.
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