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An Alternative Methodology for Estimating Credit Quality Transition Matrices


  • Jose Eduardo Gómez


  • Paola Morales Acevedo


  • Fernando Pineda


  • Nancy Zamudio



Financial institutions use credit ratings to express their risk perception abouttheir clients. Credit ratings feed their internal credit scoring models, allowingthem to evaluate the current state of the quality of their balances and to calcu-late the reserves required to provision their loan portfolios. The information theyprovide constitutes therefore a useful tool for evaluating credit demands and forasigning the corresponding interest rates to approved credits.Moreover, within a credit risk administration system, it is crucial to be able toforecast the behavior of the clientsratings in the future and their possible changesof state. From this perspective, transition matrices constitute a fundamental toolfor nancial institutions, because they measure migration probabilities amongstates. Transition probabilities are at the core of modern credit risk models andare a standard point for risk dynamics, therefore they must be estimated with rig-urous precision using the most proper techniques available.In many important economic applications (e.g. J.P. Morgans Credit Metrics),transition matrices are estimated under the Markovian assumption in a discrete-time setting using a cohort method. In a discrete and nite space setting, theprobability of migrating from state i to state j is estimated by dividing the num-ber of observed migrations from i to j in a given time period by the total numberof rms in state i at the beginning of the period. One implication of this cohortmethod is that if no rm migrates directly from state i to j during the observa-tion period, the estimate of the corresponding probability is zero. This is a notdesirable feature, specially when dealing with the estimation of rare event proba-bilities which, in case of occurring, may have a deep impact.

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  • Jose Eduardo Gómez & Paola Morales Acevedo & Fernando Pineda & Nancy Zamudio, 2007. "An Alternative Methodology for Estimating Credit Quality Transition Matrices," BORRADORES DE ECONOMIA 004395, BANCO DE LA REPÚBLICA.
  • Handle: RePEc:col:000094:004395

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    References listed on IDEAS

    1. Kiefer, Nicholas M, 1988. "Economic Duration Data and Hazard Functions," Journal of Economic Literature, American Economic Association, vol. 26(2), pages 646-679, June.
    2. 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.
    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. 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. Mahlmann, Thomas, 2006. "Estimation of rating class transition probabilities with incomplete data," Journal of Banking & Finance, Elsevier, vol. 30(11), pages 3235-3256, November.
    6. 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.
    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; ...rm-speci...c covariates; hazardfunction; transition intensities.;

    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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