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The ordered qualitative model for credit rating transitions

  • Feng, D.
  • Gourieroux, C.
  • Jasiak, J.

Information on the expected changes in credit quality of obligors is contained in credit migration matrices which trace out the movements of firms across ratings categories in a given period of time and in a given group of bond issuers. The rating matrices provided by Moody’s, Standard &Poor’s and Fitch became crucial inputs to many applications, including the assessment of risk on corporate credit portfolios (CreditVar) and credit derivatives pricing. We propose a factor probit model for modeling and prediction of credit rating matrices that are assumed to be stochastic and driven by a latent factor. The filtered latent factor path reveals the effect of the economic cycle on corporate credit ratings, and provides evidence in support of the PIT (point-in-time) rating philosophy. The factor probit model also yields the estimates of cross-sectional correlations in rating transitions that are documented empirically but not fully accounted for in the literature and in the regulatory rules established by the Basle Committee.

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Article provided by Elsevier in its journal Journal of Empirical Finance.

Volume (Year): 15 (2008)
Issue (Month): 1 (January)
Pages: 111-130

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Handle: RePEc:eee:empfin:v:15:y:2008:i:1:p:111-130
Contact details of provider: Web page: http://www.elsevier.com/locate/jempfin

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  1. 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.
  2. Gordy, Michael B., 2000. "A comparative anatomy of credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 119-149, January.
  3. Gordy, Michael B., 2003. "A risk-factor model foundation for ratings-based bank capital rules," Journal of Financial Intermediation, Elsevier, vol. 12(3), pages 199-232, July.
  4. Anil Bangia & Francis X. Diebold & Til Schuermann, 2000. "Ratings Migration and the Business Cycle, With Application to Credit Portfolio Stress Testing," Center for Financial Institutions Working Papers 00-26, Wharton School Center for Financial Institutions, University of Pennsylvania.
  5. Cheung, S., 1996. "Provincial Credit Rating in Canada: An Ordered Probit Analysis," Working Papers 96-6, Bank of Canada.
  6. Pamela Nickell & William Perraudin & Simone Varotto, 2001. "Stability of ratings transitions," Bank of England working papers 133, Bank of England.
  7. Crouhy, Michel & Galai, Dan & Mark, Robert, 2001. "Prototype risk rating system," Journal of Banking & Finance, Elsevier, vol. 25(1), pages 47-95, January.
  8. 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.
  9. Loffler, Gunter, 2004. "An anatomy of rating through the cycle," Journal of Banking & Finance, Elsevier, vol. 28(3), pages 695-720, March.
  10. Carey, Mark & Hrycay, Mark, 2001. "Parameterizing credit risk models with rating data," Journal of Banking & Finance, Elsevier, vol. 25(1), pages 197-270, January.
  11. Albanese, Claudio & Chen, Oliver X., 2006. "Implied migration rates from credit barrier models," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 607-626, February.
  12. Duffie, Darrell & Singleton, Kenneth J, 1999. "Modeling Term Structures of Defaultable Bonds," Review of Financial Studies, Society for Financial Studies, vol. 12(4), pages 687-720.
  13. Rosch, Daniel, 2005. "An empirical comparison of default risk forecasts from alternative credit rating philosophies," International Journal of Forecasting, Elsevier, vol. 21(1), pages 37-51.
  14. Treacy, William F. & Carey, Mark, 2000. "Credit risk rating systems at large US banks," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 167-201, January.
  15. Rösch, Daniel, 2003. "Correlations and Business Cycles of Credit Risk: Evidence from Bankruptcies in Germany," University of Regensburg Working Papers in Business, Economics and Management Information Systems 380, University of Regensburg, Department of Economics.
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