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Measurement and Estimation of Credit Migration Matrices

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  • Til Schuermann
  • Yusuf Jafry

Abstract

Credit migration matrices are cardinal inputs to many risk management applications. Their accurate estimation is therefore critical. We explore three approaches, cohort and two variants of duration—time homogeneous and non-homogeneous—and the resulting differences, both statistically through matrix norms and economically through credit portfolio and credit derivative models. We develop a testing procedure to assess statistically the differences between migration matrices using bootstrap techniques. The method can have substantial economic import: economic credit risk capital differences between economic regimes, recession vs. expansion, can be as large as difference implied by different estimation techniques. Ignoring the efficiency gain inherent in the duration methods by using the cohort method instead is more damaging that making a (possibly false) assumption of time-homogeneity.

Suggested Citation

  • Til Schuermann & Yusuf Jafry, 2003. "Measurement and Estimation of Credit Migration Matrices," Center for Financial Institutions Working Papers 03-08, Wharton School Center for Financial Institutions, University of Pennsylvania.
    • Handle: RePEc:wop:pennin:03-08
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    File URL: http://fic.wharton.upenn.edu/fic/papers/03/0308.pdf
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    References listed on IDEAS

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    2. Yusuf Jafry & Til Schuermann, 2003. "Metrics for Comparing Credit Migration Matrices," Center for Financial Institutions Working Papers 03-09, Wharton School Center for Financial Institutions, University of Pennsylvania.
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    Cited by:

    1. Pesaran, M. Hashem & Schuermann, Til & Treutler, Bjorn-Jakob & Weiner, Scott M., 2006. "Macroeconomic Dynamics and Credit Risk: A Global Perspective," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1211-1261, August.
    2. Yusuf Jafry & Til Schuermann, 2003. "Metrics for Comparing Credit Migration Matrices," Center for Financial Institutions Working Papers 03-09, Wharton School Center for Financial Institutions, University of Pennsylvania.
    3. Maximilian Hall & Dadang Muljawan & Lolita Moorena, 2009. "Using the artificial neural network to assess bank credit risk: a case study of Indonesia," Applied Financial Economics, Taylor & Francis Journals, vol. 19(22), pages 1825-1846.
    4. Featherstone, Allen M. & Langemeier, Michael R. & Haverkamp, Kent, 2006. "Credit Quality of Kansas Farms," 2006 Annual Meeting, February 5-8, 2006, Orlando, Florida 35309, Southern Agricultural Economics Association.
    5. Gonzalez, F. & Haas, F. & Johannes, R. & Persson, M. & Toledo, L. & Violi, R. & Zins, C. & Wieland, M., 2004. "Market dynamics associated with credit ratings: a literature review," Financial Stability Review, Banque de France, issue 4, pages 53-76, June.
    6. Bocchio, Cecilia & Crook, Jonathan & Andreeva, Galina, 2023. "The impact of macroeconomic scenarios on recurrent delinquency: A stress testing framework of multi-state models for mortgages," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1655-1677.
    7. Narcisa Kadlcakova & Joerg Keplinger, 2004. "Credit Risk and Bank Lending in the Czech Republic," Working Papers 2004/06, Czech National Bank.
    8. Jill M. Phillips & Ani L. Katchova, 2004. "Credit score migration analysis of farm businesses: conditioning on business cycles and migration trends," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 64(1), pages 1-15, May.
    9. Arnaud_de_Servigny & Norbert_Jobst, 2005. "An Empirical Analysis of Equity Default Swaps (I): Univariate Insights," International Finance 0503007, University Library of Munich, Germany.
    10. Li, Xiaofei & Escalante, Cesar L. & Dodson, Charles B., 2015. "A Credit Migration Analysis of the Financial Vitality of Female and Racial Minority Borrowers of the Farm Service Agency under Recessionary Conditions," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205038, Agricultural and Applied Economics Association.

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    More about this item

    Keywords

    Credit risk; risk management; matrix norms; bootstrapping; credit derivatives;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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