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Structural model of credit migration

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  • Chan, Ngai Hang
  • Wong, Hoi Ying
  • Zhao, Jing

Abstract

Credit migrations constitute the building blocks of modern risk management. A firm-specific structural model of credit migration that incorporates the firm’s capital structure and the risk perception of rating agencies is proposed. The proposed model employs the notion of distance-to-default, which quantifies default probability. The properties of Brownian excursions play an essential role in the analysis. The proposed model not only allows the derivation of closed-form credit transition probability, but also provides plausible explanations for certain empirical evidence, such as the default probability overlaps in ratings and the slow-to-respond feature of rating agencies. The proposed model is calibrated through simulations and applied to empirical data, which show rating agencies’ risk perceptions to be significant. The calibrated model allows calculation of the firm-specific transition probabilities of rated companies.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:11:p:3477-3490
    DOI: 10.1016/j.csda.2010.10.015
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    References listed on IDEAS

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    1. Frydman, Halina & Schuermann, Til, 2008. "Credit rating dynamics and Markov mixture models," Journal of Banking & Finance, Elsevier, vol. 32(6), pages 1062-1075, June.
    2. Feng, D. & Gourieroux, C. & Jasiak, J., 2008. "The ordered qualitative model for credit rating transitions," Journal of Empirical Finance, Elsevier, vol. 15(1), pages 111-130, January.
    3. Hoi Ying Wong & Tsz Wang Choi, 2009. "Estimating default barriers from market information," Quantitative Finance, Taylor & Francis Journals, vol. 9(2), pages 187-196.
    4. Fuertes, Ana-Maria & Kalotychou, Elena, 2007. "On sovereign credit migration: A study of alternative estimators and rating dynamics," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3448-3469, April.
    5. Brusco, Michael J. & Steinley, Douglas, 2011. "Exact and approximate algorithms for variable selection in linear discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 123-131, January.
    6. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    7. Li, Ka Leung & Wong, Hoi Ying, 2008. "Structural models of corporate bond pricing with maximum likelihood estimation," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 751-777, September.
    8. Kim, Yoonseong & Sohn, So Young, 2008. "Random effects model for credit rating transitions," European Journal of Operational Research, Elsevier, vol. 184(2), pages 561-573, January.
    9. 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.
    10. 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.
    11. 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.
    12. Jan Ericsson & Joel Reneby, 2005. "Estimating Structural Bond Pricing Models," The Journal of Business, University of Chicago Press, vol. 78(2), pages 707-735, March.
    13. 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.
    14. Jin‐Chuan Duan, 1994. "Maximum Likelihood Estimation Using Price Data Of The Derivative Contract," Mathematical Finance, Wiley Blackwell, vol. 4(2), pages 155-167, April.
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    Cited by:

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    2. Lin, X. Sheldon & Wu, Panpan & Wang, Xiao, 2016. "Move-based hedging of variable annuities: A semi-analytic approach," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 40-49.

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