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Estimating Markov Transition Matrices Using Proportions Data: An Application to Credit Risk

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  • Mr. Matthew T Jones

Abstract

This paper outlines a way to estimate transition matrices for use in credit risk modeling with a decades-old methodology that uses aggregate proportions data. This methodology is ideal for credit-risk applications where there is a paucity of data on changes in credit quality, especially at an aggregate level. Using a generalized least squares variant of the methodology, this paper provides estimates of transition matrices for the United States using both nonperforming loan data and interest coverage data. The methodology can be employed to condition the matrices on economic fundamentals and provide separate transition matrices for expansions and contractions, for example. The transition matrices can also be used as an input into other credit-risk models that use transition matrices as a basic building block.

Suggested Citation

  • Mr. Matthew T Jones, 2005. "Estimating Markov Transition Matrices Using Proportions Data: An Application to Credit Risk," IMF Working Papers 2005/219, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2005/219
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    4. Davor Kunovac, 2011. "Estimating Credit Migration Matrices with Aggregate Data – Bayesian Approach," Working Papers 30, The Croatian National Bank, Croatia.
    5. John Leventides & Konstantinos Lefkaditis & Anna Donatou & Evangelos Melas & Costas Poulios, 2023. "Development of a Transition Matrix Model of Credit Rating of Companies based on Forecasted Macro Factors: the Case of Greece," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 13(5), pages 1-3.
    6. Rafael González & Christopher Stehr, 2015. "Participating In International Study Tours Leads To Entrepreneurial Success Abroad – A Research On The Positive Effects Of International Exchange Tours," GUT FME Conference Publications, in: Katarzyna Stankiewicz (ed.),Contemporary Issues and Challenges in Human Resource Management, chapter 15, pages 165-175, Faculty of Management and Economics, Gdansk University of Technology.
    7. Rowden, Jessica & Lloyd, David J.B. & Gilbert, Nigel, 2014. "A model of political voting behaviours across different countries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 609-625.
    8. Carmela Cappelli & Francesca Iorio & Angela Maddaloni & Pierpaolo D’Urso, 2021. "Atheoretical Regression Trees for classifying risky financial institutions," Annals of Operations Research, Springer, vol. 299(1), pages 1357-1377, April.
    9. Pasanisi, Alberto & Fu, Shuai & Bousquet, Nicolas, 2012. "Estimating discrete Markov models from various incomplete data schemes," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2609-2625.
    10. Beate Jahn & Christina Kurzthaler & Jagpreet Chhatwal & Elamin H. Elbasha & Annette Conrads-Frank & Ursula Rochau & Gaby Sroczynski & Christoph Urach & Marvin Bundo & Niki Popper & Uwe Siebert, 2019. "Alternative Conversion Methods for Transition Probabilities in State-Transition Models: Validity and Impact on Comparative Effectiveness and Cost-Effectiveness," Medical Decision Making, , vol. 39(5), pages 509-522, July.

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    WP; real gross domestic product;

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