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


  • Matthew T Jones


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

  • Matthew T Jones, 2005. "Estimating Markov Transition Matrices Using Proportions Data; An Application to Credit Risk," IMF Working Papers 05/219, International Monetary Fund.
  • Handle: RePEc:imf:imfwpa:05/219

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    References listed on IDEAS

    1. Gregor Andrade & Steven N. Kaplan, 1998. "How Costly is Financial (Not Economic) Distress? Evidence from Highly Leveraged Transactions that Became Distressed," Journal of Finance, American Finance Association, vol. 53(5), pages 1443-1493, October.
    2. Kelton, Christina M L & Kelton, W David, 1982. "Advertising and Intraindustry Brand Shift in the U.S. Brewing Industry," Journal of Industrial Economics, Wiley Blackwell, vol. 30(3), pages 293-303, March.
    3. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    4. 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.
    5. Altman, Edward I. & Saunders, Anthony, 1997. "Credit risk measurement: Developments over the last 20 years," Journal of Banking & Finance, Elsevier, vol. 21(11-12), pages 1721-1742, December.
    6. MacRae, Elizabeth Chase, 1977. "Estimation of Time-Varying Markov Processes with Aggregate Data," Econometrica, Econometric Society, vol. 45(1), pages 183-198, January.
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    Cited by:

    1. Karol Flisikowski & Dagmara Nikulin, 2015. "Workforce Mobility Against The Background Of Labour Market Duality Theory – The Example Of Selected Oecd Countries," GUT FME Conference Publications,in: Katarzyna Stankiewicz (ed.), Contemporary Issues and Challenges in Human Resource Management, chapter 2, pages 9-17 Faculty of Management and Economics, Gdansk University of Technology.
    2. Davor Kunovac, 2011. "Estimating Credit Migration Matrices with Aggregate Data – Bayesian Approach," Working Papers 30, The Croatian National Bank, Croatia.
    3. 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.
    4. 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.
    5. 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.

    More about this item


    Credit risk; Loans; United States; Markov transition matrix; nonperforming loans; interest coverage; probabilities; probability; probability model; interest coverage ratio; banking; Estimation;

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