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An Alternative Methodology for Estimating Credit Quality Transition Matrices


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  • 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 clients�ratings 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. Morgan�s 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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Paper provided by BANCO DE LA REPÚBLICA in its series BORRADORES DE ECONOMIA with number 004395.

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Length: 19
Date of creation: 23 Dec 2007
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Handle: RePEc:col:000094:004395

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Keywords: Firms; macroeconomic variables; ...rm-speci...c covariates; hazardfunction; transition intensities.;

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  1. Kiefer, Nicholas M. & Larson, C. Erik, 2006. "A Simulation Estimator for Testing the Time Homogeneity of Credit Rating Transition," Working Papers, Cornell University, Center for Analytic Economics 06-10, Cornell University, Center for Analytic Economics.
  2. Gomez-Gonzalez, Jose E. & Kiefer, Nicholas M., 2006. "Bank Failure: Evidence from the Colombia Financial Crisis," Working Papers, Cornell University, Center for Analytic Economics 06-12, Cornell University, Center for Analytic Economics.
  3. José E. Gómez-Gonzalez & Nicholas M. Kiefer, 2007. "Evidence of non-Markovian behavior in the process of bank rating migrations," BORRADORES DE ECONOMIA 003961, BANCO DE LA REPÚBLICA.
  4. Kiefer, Nicholas M, 1988. "Economic Duration Data and Hazard Functions," Journal of Economic Literature, American Economic Association, vol. 26(2), pages 646-79, June.
  5. Lando, David & Skodeberg, Torben M., 2002. "Analyzing rating transitions and rating drift with continuous observations," Journal of Banking & Finance, Elsevier, Elsevier, vol. 26(2-3), pages 423-444, March.
  6. 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, Wharton School Center for Financial Institutions, University of Pennsylvania 00-26, Wharton School Center for Financial Institutions, University of Pennsylvania.
  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. Mahlmann, Thomas, 2006. "Estimation of rating class transition probabilities with incomplete data," Journal of Banking & Finance, Elsevier, Elsevier, vol. 30(11), pages 3235-3256, November.
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Cited by:
  1. Ángela González Arbeláez, 2010. "Determinantes del riesgo del crédito comercial en Colombia," VNIVERSITAS ECONÓMICA 008215, UNIVERSIDAD JAVERIANA - BOGOTÁ.


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