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On comparing the accuracy of default predictions in the rating industry

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Author Info

  • 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.

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Bibliographic Info

Paper provided by Business and Social Statistics Department, Technische Universität Dortmund in its series Working Papers with number 2.

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Length: 26 pages
Date of creation:
Date of revision: Oct 2006
Publication status: Published in Empirical Economics, May 2008, pages 343-356
Handle: RePEc:dor:wpaper:2

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Keywords: credit rating; probability forecasts; calibration;

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References

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  1. Prof. Dr. Walter Krämer, . "On the ordering of probability forecasts," Working Papers 1, Business and Social Statistics Department, Technische Universität Dortmund, revised May 2003.
  2. Krämer, Walter, 2003. "Evaluating probability forecasts in terms of refinement and strictly proper scoring rules," Technical Reports 2003,24, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
  3. Carey, Mark & Hrycay, Mark, 2001. "Parameterizing credit risk models with rating data," Journal of Banking & Finance, Elsevier, vol. 25(1), pages 197-270, January.
  4. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
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Cited by:
  1. 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.
  2. Darrel Duffie & Leandro Saita & Ke Wang, 2005. "Multi-Period Corporate Default Prediction With Stochastic Covariates," CARF F-Series CARF-F-047, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
  3. Simon Cornée, 2014. "Soft Information and Default Prediction in Cooperative and Social Banks," Economics Working Paper Archive (University of Rennes 1 & University of Caen) 201402, Center for Research in Economics and Management (CREM), University of Rennes 1, University of Caen and CNRS.
  4. Christophe Godlewski, 2004. "Are Bank Ratings Coherent with Bank Default Probabilities in Emerging Market Economies ?," Finance 0409023, EconWPA.
  5. Andre Güttler & Peter Raupach, 2010. "The Impact of Downward Rating Momentum," Journal of Financial Services Research, Springer, vol. 37(1), pages 1-23, February.
  6. Kurt Hornik & Rainer Jankowitsch & Manuel Lingo & Stefan Pichler & Gerhard Winkler, 2010. "Determinants of heterogeneity in European credit ratings," Financial Markets and Portfolio Management, Springer, vol. 24(3), pages 271-287, September.

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