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An Alternative Methodology for Estimating Credit Quality Transition Matrices


  • Jose E. Gómez


  • Paola Morales


  • Fernando Pineda




This study presents an alternative way of estimating credit transition matrices using a hazard function model. The model is useful both for testing the validity of the Markovian assumption, frequently made in credit rating applications, and also for estimating transition matrices conditioning on firm-specific and macroeconomic covariates that influence the migration process. The model presented in the paper is likely to be useful in other applications, though we would hesitate to extrapolate numerical values of coefficients outside of our application. Transition matrices estimated this way may be an important tool for a credit risk administration system, in the sense that with them a practitioner can easily forecast the behavior of the clients´ratings in the future and their possible changes of state

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  • Jose E. Gómez & Paola Morales & Fernando Pineda &, 2007. "An Alternative Methodology for Estimating Credit Quality Transition Matrices," Borradores de Economia 478, Banco de la Republica de Colombia.
  • Handle: RePEc:bdr:borrec:478

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    References listed on IDEAS

    1. Mahlmann, Thomas, 2006. "Estimation of rating class transition probabilities with incomplete data," Journal of Banking & Finance, Elsevier, vol. 30(11), pages 3235-3256, November.
    2. Kiefer, Nicholas M, 1988. "Economic Duration Data and Hazard Functions," Journal of Economic Literature, American Economic Association, vol. 26(2), pages 646-679, June.
    3. José E. Gómez-González & Nicholas M. Kiefer., 2009. "Evidence of Non-Markovian Behavior in the Process of Bank Rating Migrations," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 46(133), pages 33-50.
    4. José E. Gómez-Gonzalez & Nicholas M. Kiefer, 2009. "Bank Failure: Evidence From The Colombian Financial Crisis," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 3(2), pages 15-31.
    5. Kiefer, Nicholas M. & Larson, C. Erik, 2007. "A simulation estimator for testing the time homogeneity of credit rating transitions," Journal of Empirical Finance, Elsevier, vol. 14(5), pages 818-835, December.
    6. 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.
    7. Audretsch, David B & Mahmood, Talat, 1995. "New Firm Survival: New Results Using a Hazard Function," The Review of Economics and Statistics, MIT Press, vol. 77(1), pages 97-103, February.
    8. 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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    Cited by:

    1. Angela González Arbeláez, "undated". "Determinantes del riesgo de crédito comercial en Colombia," Temas de Estabilidad Financiera 045, Banco de la Republica de Colombia.

    More about this item


    Firms; macroeconomic variables; firm-specific covariates; hazard function; transition intensities. Classification JEL: C4; E44; G21; G23; G38.;

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • G38 - Financial Economics - - Corporate Finance and Governance - - - Government Policy and Regulation


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