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Estimating Markov Transition Matrices Using Proportions Data

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  • Matthew T. Jones
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    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.

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    File URL: http://www.imf.org/external/pubs/cat/longres.aspx?sk=18387
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    Bibliographic Info

    Paper provided by International Monetary Fund in its series IMF Working Papers with number 05/219.

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    Length: 27
    Date of creation: 01 Nov 2005
    Date of revision:
    Handle: RePEc:imf:imfwpa:05/219

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    Related research

    Keywords: Credit risk; Data analysis; probabilities; probability; probability model; interest coverage ratio; banking; equation; interest expense; nonperforming loan; statistics; time series; markov process; heteroscedasticity; covariance; consistent estimate; equations; bank loans; banking crises; markov processes; markov chains; standard errors; bank balance sheets; statistic; short term debt; bank insolvency; random variable; cointegration; statistical tests; samples; bank soundness; survey; correlation; banking system; regulatory forbearance; sample selection; income statement; probability distribution; loan classification; banker; stationary process; bank regulators; sample size; asset classification;

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    1. MacRae, Elizabeth Chase, 1977. "Estimation of Time-Varying Markov Processes with Aggregate Data," Econometrica, Econometric Society, vol. 45(1), pages 183-98, January.
    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. 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.
    4. 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.
    5. 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 357-84, March.
    6. 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.
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