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On comparing the accuracy of default predictions in the rating industry

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  • Prof. Dr. Walter Krämer

    (Faculty of Statistics, Dortmund University of Technology)

  • Andre Güttler

    (Universität Frankfurt, Finance Department)

Abstract

We consider 1927 borrowers from 54 countries who had a credit rating by both Moody's and S&P at the end of 1998, and their subsequent default history up to the end of 2002. Viewing bond ratings as predicted probabilities of default, we consider partial orderings among competing probability forecasters and show that Moody's and S&P cannot be ordered according to any of these. Therefore, the relative performance of the agencies depends crucially on the way in which probability predictions are compared.

Suggested Citation

  • Prof. Dr. Walter Krämer & Andre Güttler, "undated". "On comparing the accuracy of default predictions in the rating industry," Working Papers 2, Business and Social Statistics Department, Technische Universität Dortmund, revised Oct 2006.
    • Handle: RePEc:dor:wpaper:2
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    References listed on IDEAS

    as
    1. Prof. Dr. Walter Krämer, "undated". "On the ordering of probability forecasts," Working Papers 1, Business and Social Statistics Department, Technische Universität Dortmund, revised May 2003.
    2. Walter Krämer, 2006. "Evaluating probability forecasts in terms of refinement and strictly proper scoring rules," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(3), pages 223-226.
    3. Moon, Choon-Geol & Stotsky, Janet G, 1993. "Testing the Differences between the Determinants of Moody's and Standard & Poor's Ratings: An Application of Smooth Simulated Maximum Likelihood Estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(1), pages 51-69, Jan.-Marc.
    4. Crouhy, Michel & Galai, Dan & Mark, Robert, 2001. "Prototype risk rating system," Journal of Banking & Finance, Elsevier, vol. 25(1), pages 47-95, January.
    5. Louis H. Ederington & Jess B. Yawitz & Brian E. Roberts, 1987. "The Informational Content Of Bond Ratings," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 10(3), pages 211-226, September.
    6. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    7. Carey, Mark & Hrycay, Mark, 2001. "Parameterizing credit risk models with rating data," Journal of Banking & Finance, Elsevier, vol. 25(1), pages 197-270, January.
    8. Robert L. Winkler, 1994. "Evaluating Probabilities: Asymmetric Scoring Rules," Management Science, INFORMS, vol. 40(11), pages 1395-1405, November.
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    Cited by:

    1. Kurt Hornik & Rainer Jankowitsch & Manuel Lingo & Stefan Pichler & Gerhard Winkler, 2010. "Determinants of heterogeneity in European credit ratings," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 24(3), pages 271-287, September.
    2. Simon Cornée, 2014. "Soft Information and Default Prediction in Cooperative and Social Banks," Journal of Entrepreneurial and Organizational Diversity, European Research Institute on Cooperative and Social Enterprises, vol. 3(1), pages 89-103, June.
    3. Morkoetter, Stefan & Stebler, Roman & Westerfeld, Simone, 2017. "Competition in the credit rating Industry: Benefits for investors and issuers," Journal of Banking & Finance, Elsevier, vol. 75(C), pages 235-257.
    4. Chang, Charles & Fuh, Cheng-Der & Kao, Chu-Lan Michael, 2017. "Reading between the ratings: Modeling residual credit risk and yield overlap," Journal of Banking & Finance, Elsevier, vol. 81(C), pages 114-135.
    5. Orth, Walter, 2010. "The predictive accuracy of credit ratings: Measurement and statistical inference," MPRA Paper 30148, University Library of Munich, Germany, revised 16 Feb 2011.
    6. Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007. "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Elsevier, vol. 83(3), pages 635-665, March.
    7. Andre Güttler & Peter Raupach, 2010. "The Impact of Downward Rating Momentum," Journal of Financial Services Research, Springer;Western Finance Association, vol. 37(1), pages 1-23, February.
    8. Christophe Godlewski, 2004. "Are Bank Ratings Coherent with Bank Default Probabilities in Emerging Market Economies ?," Finance 0409023, University Library of Munich, Germany.

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    Keywords

    credit rating; probability forecasts; calibration;
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