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A hidden Markov model of credit quality

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

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.

Suggested Citation

  • Korolkiewicz, Malgorzata W. & Elliott, Robert J., 2008. "A hidden Markov model of credit quality," Journal of Economic Dynamics and Control, Elsevier, vol. 32(12), pages 3807-3819, December.
    • Handle: RePEc:eee:dyncon:v:32:y:2008:i:12:p:3807-3819
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    References listed on IDEAS

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    1. Bangia, Anil & Diebold, Francis X. & Kronimus, Andre & Schagen, Christian & Schuermann, Til, 2002. "Ratings migration and the business cycle, with application to credit portfolio stress testing," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 445-474, March.
    2. Carey, Mark & Hrycay, Mark, 2001. "Parameterizing credit risk models with rating data," Journal of Banking & Finance, Elsevier, vol. 25(1), pages 197-270, January.
    3. 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.
    4. 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.
    5. 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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    Citations

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    Cited by:

    1. Wozabal, David & Hochreiter, Ronald, 2012. "A coupled Markov chain approach to credit risk modeling," Journal of Economic Dynamics and Control, Elsevier, vol. 36(3), pages 403-415.
    2. Puneet Pasricha & Dharmaraja Selvamuthu & Guglielmo D’Amico & Raimondo Manca, 2020. "Portfolio optimization of credit risky bonds: a semi-Markov process approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-14, December.
    3. D. V. Boreiko & Y. M. Kaniovski & G. Ch. Pflug, 2016. "Modeling dependent credit rating transitions: a comparison of coupling schemes and empirical evidence," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 24(4), pages 989-1007, December.
    4. Areski Cousin & Jérôme Lelong & Tom Picard, 2022. "Rating transitions forecasting: a filtering approach," Working Papers hal-03347521, HAL.
    5. Liu, Wei-han, 2018. "Hidden Markov model analysis of extreme behaviors of foreign exchange rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1007-1019.
    6. Areski Cousin & J'er^ome Lelong & Tom Picard, 2021. "Rating transitions forecasting: a filtering approach," Papers 2109.10567, arXiv.org, revised Jun 2023.
    7. D. V. Boreiko & Y. M. Kaniovski & G. Ch. Pflug, 2017. "Numerical Modeling of Dependent Credit Rating Transitions with Asynchronously Moving Industries," Computational Economics, Springer;Society for Computational Economics, vol. 49(3), pages 499-516, March.
    8. Dmitri Boreiko & Serguei Kaniovski & Yuri Kaniovski & Georg Ch. Pflug, 2018. "Business Cycles and Conditional Credit-Rating Migration Matrices," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 8(04), pages 1-19, December.
    9. Damian Camilla & Eksi Zehra & Frey Rüdiger, 2018. "EM algorithm for Markov chains observed via Gaussian noise and point process information: Theory and case studies," Statistics & Risk Modeling, De Gruyter, vol. 35(1-2), pages 51-72, January.
    10. Lu, Jianjun & Tokinaga, Shozo, 2014. "Estimation of state changes in system descriptions for dynamic Bayesian networks by using a genetic procedure and particle filters," Economic Modelling, Elsevier, vol. 39(C), pages 138-145.
    11. Jeffrey R. Stokes, 2023. "A nonlinear inversion procedure for modeling the effects of economic factors on credit risk migration," Review of Quantitative Finance and Accounting, Springer, vol. 61(3), pages 855-878, October.
    12. Yun-Ling Wu & Cheng-Huang Tung & Chun-Chang Lee, 2017. "The Power of a Leading Indicators Fluctuation Trend for Forecasting Taiwans Real Estate Business Cycle: An Application of a Hidden Markov Model," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 7(1), pages 81-98, January.
    13. Areski Cousin & Jérôme Lelong & Tom Picard, 2023. "Rating transitions forecasting: a filtering approach," Post-Print hal-03347521, HAL.

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