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On a multivariate Markov chain model for credit risk measurement

Author

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  • Tak-Kuen Siu
  • Wai-Ki Ching
  • S. Eric Fung
  • Michael Ng

Abstract

In this paper, we use credibility theory to estimate credit transition matrices in a multivariate Markov chain model for credit rating. A transition matrix is estimated by a linear combination of the prior estimate of the transition matrix and the empirical transition matrix. These estimates can be easily computed by solving a set of linear programming (LP) problems. The estimation procedure can be implemented easily on Excel spreadsheets without requiring much computational effort and time. The number of parameters is O(s2m2), where s is the dimension of the categorical time series for credit ratings and m is the number of possible credit ratings for a security. Numerical evaluations of credit risk measures based on our model are presented.

Suggested Citation

  • Tak-Kuen Siu & Wai-Ki Ching & S. Eric Fung & Michael Ng, 2005. "On a multivariate Markov chain model for credit risk measurement," Quantitative Finance, Taylor & Francis Journals, vol. 5(6), pages 543-556.
    • Handle: RePEc:taf:quantf:v:5:y:2005:i:6:p:543-556
    DOI: 10.1080/14697680500383714
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    Cited by:

    1. Hao Liu & Shijin Chen, 2015. "Credit Risk Measurement Based on the Markov Chain," Business and Management Research, Business and Management Research, Sciedu Press, vol. 4(3), pages 32-42, September.

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