IDEAS home Printed from https://ideas.repec.org/p/ecl/corcae/06-10.html
   My bibliography  Save this paper

A Simulation Estimator for Testing the Time Homogeneity of Credit Rating Transition

Author

Listed:
  • Kiefer, Nicholas M.

    (Cornell U and US Department of the Treasury)

  • Larson, C. Erik

    (Fannie Mae)

Abstract

The measurement of credit quality is at the heart of the models designed to assess the reserves and capital needed to support the risks of both individual credits and portfolios of credit instruments. A popular specification for credit- rating transitions is the simple, time-homogeneous Markov model. While the Markov specification cannot really describe processes in the long run, it may be useful for adequately describing short-run changes in portfolio risk. In this specification, the entire stochastic process can be characterized in terms of estimated transition probabilities. However, the simple homogeneous Markovian transition framework is restrictive. We propose a test of the null hypotheses of time-homogeneity that can be performed on the sorts of data often reported. We apply the tests to 4 data sets, on commercial paper, sovereign debt, municipal bonds and S&P Corporates. The results indicate that commercial paper looks Markovian on a 30-day time scale for up to 6 months; sovereign debt also looks Markovian (perhaps due to a small sample size); municipals are well-modeled by the Markov specification for up to 5 years, but could probably benefit from frequent updating of the estimated transition matrix or from more sophisticated modeling, and S&P Corporate ratings are approximately Markov over 3 transitions but not 4.

Suggested Citation

  • Kiefer, Nicholas M. & Larson, C. Erik, 2006. "A Simulation Estimator for Testing the Time Homogeneity of Credit Rating Transition," Working Papers 06-10, Cornell University, Center for Analytic Economics.
  • Handle: RePEc:ecl:corcae:06-10
    as

    Download full text from publisher

    File URL: https://cae.economics.cornell.edu/06-10.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Gourieroux, C & Monfort, A & Renault, E, 1993. "Indirect Inference," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 85-118, Suppl. De.
    2. Jafry, Yusuf & Schuermann, Til, 2004. "Measurement, estimation and comparison of credit migration matrices," Journal of Banking & Finance, Elsevier, vol. 28(11), pages 2603-2639, November.
    3. Gallant, A. Ronald & Tauchen, George, 1996. "Which Moments to Match?," Econometric Theory, Cambridge University Press, vol. 12(04), pages 657-681, October.
    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. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. Weißbach, Rafael & Mollenhauer, Thomas, 2011. "Modelling Rating Transitions," Annual Conference 2011 (Frankfurt, Main): The Order of the World Economy - Lessons from the Crisis 48698, Verein für Socialpolitik / German Economic Association.
    4. Chateau, Jean-Pierre D., 2011. "Contribution à la réglementation de Bâle-3 : de la consistance interne du continuum du crédit commercial en marquant à la « valeur de modèle » le risque de crédit des engagements de crédit," L'Actualité Economique, Société Canadienne de Science Economique, vol. 87(4), pages 445-479, décembre.
    5. Wozabal, David & Hochreiter, Ronald, 2012. "A coupled Markov chain approach to credit risk modeling," Journal of Economic Dynamics and Control, Elsevier, vol. 36(3), pages 403-415.
    6. Weißbach, Rafael & Walter, Ronja, 2008. "A likelihood ratio test for stationarity of rating transitions," Technical Reports 2008,27, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    7. Weißbach, Rafael & Walter, Ronja, 2010. "A likelihood ratio test for stationarity of rating transitions," Journal of Econometrics, Elsevier, vol. 155(2), pages 188-194, April.
    8. Weißbach, Rafael & Dette, Holger, 2008. "Bias in nearest-neighbor hazard estimation," Technical Reports 2008,15, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    9. Chateau, John-Peter D., 2009. "Marking-to-model credit and operational risks of loan commitments: A Basel-2 advanced internal ratings-based approach," International Review of Financial Analysis, Elsevier, vol. 18(5), pages 260-270, December.
    10. Weißbach, Rafael & Strohecker, Fynn, 2016. "Modeling rating transitions with instantaneous default," Economics Letters, Elsevier, vol. 145(C), pages 38-40.
    11. Dimitris Gavalas & Theodore Syriopoulos, 2014. "Bank Credit Risk Management and Rating Migration Analysis on the Business Cycle," International Journal of Financial Studies, MDPI, Open Access Journal, vol. 2(1), pages 1-22, March.

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ecl:corcae:06-10. 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: (). General contact details of provider: http://edirc.repec.org/data/cacorus.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.