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Credit rating dynamics and Markov mixture models

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  • Frydman, Halina
  • Schuermann, Til

Abstract

Despite mounting evidence to the contrary, credit migration matrices, used in many credit risk and pricing applications, are typically assumed to be generated by a simple Markov process. Based on empirical evidence, we propose a parsimonious model that is a mixture of (two) Markov chains, where the mixing is on the speed of movement among credit ratings. We estimate this model using credit rating histories and show that the mixture model statistically dominates the simple Markov model and that the differences between two models can be economically meaningful. The non-Markov property of our model implies that the future distribution of a firm's ratings depends not only on its current rating but also on its past rating history. Indeed we find that two firms with identical current credit ratings can have substantially different transition probability vectors. We also find that conditioning on the state of the business cycle or industry group does not remove the heterogeneity with respect to the rate of movement. We go on to compare the performance of mixture and Markov chain using out-of-sample predictions.

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Bibliographic Info

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

Volume (Year): 32 (2008)
Issue (Month): 6 (June)
Pages: 1062-1075

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Handle: RePEc:eee:jbfina:v:32:y:2008:i:6:p:1062-1075

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References

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  1. Frydman, Halina, 2005. "Estimation in the Mixture of Markov Chains Moving With Different Speeds," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1046-1053, September.
  2. 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.
  3. 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.
  4. Christensen, Jens H.E. & Hansen, Ernst & Lando, David, 2004. "Confidence sets for continuous-time rating transition probabilities," Journal of Banking & Finance, Elsevier, vol. 28(11), pages 2575-2602, November.
  5. Nickell, Pamela & Perraudin, William & Varotto, Simone, 2000. "Stability of rating transitions," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 203-227, January.
  6. Jarrow, Robert A & Turnbull, Stuart M, 1995. " Pricing Derivatives on Financial Securities Subject to Credit Risk," Journal of Finance, American Finance Association, vol. 50(1), pages 53-85, March.
  7. 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.
  8. Jafry, Yusuf & Schuermann, Til, 2004. "Measurement, estimation and comparison of credit migration matrices," Journal of Banking & Finance, Elsevier, vol. 28(11), pages 2603-2639, November.
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Citations

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Cited by:
  1. Tsaig, Yaakov & Levy, Amnon & Wang, Yashan, 2011. "Analyzing the impact of credit migration in a portfolio setting," Journal of Banking & Finance, Elsevier, vol. 35(12), pages 3145-3157.
  2. Bannier, Christina E. & Hirsch, Christian W., 2010. "The economic function of credit rating agencies - What does the watchlist tell us?," Journal of Banking & Finance, Elsevier, vol. 34(12), pages 3037-3049, December.
  3. May, Anthony D., 2010. "The impact of bond rating changes on corporate bond prices: New evidence from the over-the-counter market," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2822-2836, November.
  4. Weißbach, Rafael & Mollenhauer, Thomas, 2011. "Modelling Rating Transitions," Annual Conference 2011 (Frankfurt, Main): The Order of the World Economy - Lessons from the Crisis 48698, Verein für Socialpolitik / German Economic Association.
  5. Sumon Bhaumik & John S. Landon-Lane, 2007. "Directional Mobility of Ratings," William Davidson Institute Working Papers Series wp900, William Davidson Institute at the University of Michigan.
  6. Figlewski, Stephen & Frydman, Halina & Liang, Weijian, 2012. "Modeling the effect of macroeconomic factors on corporate default and credit rating transitions," International Review of Economics & Finance, Elsevier, vol. 21(1), pages 87-105.
  7. David Wozabal & Ronald Hochreiter, 2009. "A Coupled Markov Chain Approach to Credit Risk Modeling," Papers 0911.3802, arXiv.org, revised Jan 2014.
  8. Chan, Ngai Hang & Wong, Hoi Ying & Zhao, Jing, 2012. "Structural model of credit migration," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3477-3490.
  9. Dimitris Gavalas & Theodore Syriopoulos, 2014. "Bank Credit Risk Management and Rating Migration Analysis on the Business Cycle," International Journal of Financial Studies, MDPI, Open Access Journal, vol. 2(1), pages 122-143, March.
  10. 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.
  11. Hill, Paula & Brooks, Robert & Faff, Robert, 2010. "Variations in sovereign credit quality assessments across rating agencies," Journal of Banking & Finance, Elsevier, vol. 34(6), pages 1327-1343, June.
  12. Paul Mizen & Serafeim Tsoukas, 2011. "Forecasting US bond default ratings allowing for previous and initial state dependence in an ordered probit model," Working Papers 2011_19, Business School - Economics, University of Glasgow.
  13. Xing, Haipeng & Sun, Ning & Chen, Ying, 2012. "Credit rating dynamics in the presence of unknown structural breaks," Journal of Banking & Finance, Elsevier, vol. 36(1), pages 78-89.
  14. Dorfleitner, G. & Priberny, C., 2013. "A quantitative model for structured microfinance," The Quarterly Review of Economics and Finance, Elsevier, vol. 53(1), pages 12-22.
  15. Chateau, John-Peter D., 2009. "Marking-to-model credit and operational risks of loan commitments: A Basel-2 advanced internal ratings-based approach," International Review of Financial Analysis, Elsevier, vol. 18(5), pages 260-270, December.
  16. Louis, Philippe & Van Laere, Elisabeth & Baesens, Bart, 2013. "Understanding and predicting bank rating transitions using optimal survival analysis models," Economics Letters, Elsevier, vol. 119(3), pages 280-283.

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