An Alternative Methodology for Estimating Credit Quality Transition Matrices
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.
|Date of creation:||23 Dec 2007|
|Date of revision:|
|Contact details of provider:|| |
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- José E. Gómez González & Nicholas M. Kiefer, 2007. "Evidence of non-Markovian behavior in the process of bank rating migrations," BORRADORES DE ECONOMIA 004016, BANCO DE LA REPÚBLICA.
- 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.
- José E.Gómez González & Nicholas M. Kiefer, . "Evidence of non-Markovian behavior in the process of bank rating migrations," Borradores de Economia 448, Banco de la Republica de Colombia.
- Kiefer, Nicholas M. & Larson, C. Erik, 2006.
"A Simulation Estimator for Testing the Time Homogeneity of Credit Rating Transition,"
06-10, Cornell University, Center for Analytic Economics.
- 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.
- 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.
- 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.
- Kiefer, Nicholas M, 1988. "Economic Duration Data and Hazard Functions," Journal of Economic Literature, American Economic Association, vol. 26(2), pages 646-79, June.
- Mahlmann, Thomas, 2006. "Estimation of rating class transition probabilities with incomplete data," Journal of Banking & Finance, Elsevier, vol. 30(11), pages 3235-3256, November.
- 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.
- 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.
- Gomez-Gonzalez, Jose E. & Kiefer, Nicholas M., 2006. "Bank Failure: Evidence from the Colombia Financial Crisis," Working Papers 06-12, Cornell University, Center for Analytic Economics.
When requesting a correction, please mention this item's handle: RePEc:col:000094:004395. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Clorith Angélica Bahos Olivera)
If references are entirely missing, you can add them using this form.