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The predictive accuracy of credit ratings: Measurement and statistical inference

  • Orth, Walter
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    Credit ratings are ordinal predictions for the default risk of an obligor. To evaluate the accuracy of such predictions commonly used measures are the Accuracy Ratio or, equivalently, the Area under the ROC curve. The disadvantage of these measures is that they treat default as a binary variable thereby neglecting the timing of the default events and also not using the full information from censored observations. We present an alternative measure that is related to the Accuracy Ratio but does not suffer from these drawbacks. As a second contribution, we study statistical inference for the Accuracy Ratio and the proposed measure in the case of multiple cohorts of obligors with overlapping lifetimes. We derive methods that use more sample information and lead to more powerful tests than alternatives that filter just the independent part of the dataset. All procedures are illustrated in the empirical section using a dataset of S\&P Long Term Credit Ratings.

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    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 30148.

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    Date of creation: 22 Mar 2010
    Date of revision: 16 Feb 2011
    Handle: RePEc:pra:mprapa:30148
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    1. Roger Newson, 2006. "Confidence intervals for rank statistics: Somers' D and extensions," Stata Journal, StataCorp LP, vol. 6(3), pages 309-334, September.
    2. Prof. Dr. Walter Krämer & Andre Güttler, . "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.
    3. 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.
    4. Andre Güttler & Peter Raupach, 2010. "The Impact of Downward Rating Momentum," Journal of Financial Services Research, Springer, vol. 37(1), pages 1-23, February.
    5. C. A. Field & A. H. Welsh, 2007. "Bootstrapping clustered data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(3), pages 369-390.
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