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The predictive accuracy of credit ratings: Measurement and statistical inference

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  • Orth, Walter

Abstract

Credit ratings are ordinal predictions of the default risk of an obligor. The most commonly used measure for evaluating their predictive accuracy is the Accuracy Ratio, or equivalently, the area under the ROC curve. The disadvantages of these measures are that they treat default as a binary variable, thus neglecting the timing of default events, and they fail to use all of the information available from censored observations. We present an alternative measure which 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 which use more sample information and lead to tests which are more powerful than alternatives which filter just the independent part of the dataset. All procedures are illustrated in the empirical section using a dataset of S&P Credit Ratings.

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  • Orth, Walter, 2012. "The predictive accuracy of credit ratings: Measurement and statistical inference," International Journal of Forecasting, Elsevier, vol. 28(1), pages 288-296.
    • Handle: RePEc:eee:intfor:v:28:y:2012:i:1:p:288-296
    DOI: 10.1016/j.ijforecast.2011.07.004
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    References listed on IDEAS

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    Cited by:

    1. Orth, Walter, 2013. "Multi-period credit default prediction with time-varying covariates," Journal of Empirical Finance, Elsevier, vol. 21(C), pages 214-222.
    2. Berloco, Claudia & Argiento, Raffaele & Montagna, Silvia, 2023. "Forecasting short-term defaults of firms in a commercial network via Bayesian spatial and spatio-temporal methods," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1065-1077.
    3. Shen, Feng & Zhang, Xin & Wang, Run & Lan, Dao & Zhou, Wei, 2022. "Sequential optimization three-way decision model with information gain for credit default risk evaluation," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1116-1128.
    4. Balios, Dimitris & Thomadakis, Stavros & Tsipouri, Lena, 2016. "Credit rating model development: An ordered analysis based on accounting data," Research in International Business and Finance, Elsevier, vol. 38(C), pages 122-136.
    5. Nehrebecka Natalia, 2018. "An Evaluation of the Discriminatory Power of Selected Polish Bankruptcy Prediction Models As Part of the Validation Process," Financial Sciences. Nauki o Finansach, Sciendo, vol. 23(4), pages 63-88, December.

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