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A hidden Markov model of credit quality

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  • Korolkiewicz, Malgorzata W.
  • Elliott, Robert J.
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    Abstract

    This paper presents a hidden Markov model of credit quality dynamics, and highlights the use of filtering-based estimation methods for models of this kind. We suppose that the Markov chain governing the 'true' credit quality evolution is hidden in 'noisy' or incomplete observations represented by posted credit ratings. Parameters of the model, namely credit transition probabilities, are estimated using the EM algorithm. Filtering methods provide recursive updates of optimal estimates so the model is 'self-calibrating'. The estimation procedure is illustrated with an application to a data set of Standard & Poor's credit ratings.

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    File URL: http://www.sciencedirect.com/science/article/B6V85-4S8CR29-2/2/345c9443ee72d4157d5164816db88df3
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    Bibliographic Info

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

    Volume (Year): 32 (2008)
    Issue (Month): 12 (December)
    Pages: 3807-3819

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    Handle: RePEc:eee:dyncon:v:32:y:2008:i:12:p:3807-3819

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

    Related research

    Keywords: Credit quality Filtering Hidden Markov models EM algorithm;

    References

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    1. 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.
    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. Loffler, Gunter, 2005. "Avoiding the rating bounce: why rating agencies are slow to react to new information," Journal of Economic Behavior & Organization, Elsevier, vol. 56(3), pages 365-381, March.
    4. 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.
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    Cited by:
    1. David Wozabal & Ronald Hochreiter, 2009. "A Coupled Markov Chain Approach to Credit Risk Modeling," Papers 0911.3802, arXiv.org, revised Jan 2014.

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