IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v28y2012i1p288-296.html
   My bibliography  Save this article

The predictive accuracy of credit ratings: Measurement and statistical inference

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207011001014
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2011.07.004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. Härdle, Wolfgang & Horowitz, Joel L. & Kreiss, Jens-Peter, 2001. "Bootstrap methods for time series," SFB 373 Discussion Papers 2001,59, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    4. Roger Newson, 2006. "Confidence intervals for rank statistics: Percentile slopes, differences, and ratios," Stata Journal, StataCorp LP, vol. 6(4), pages 497-520, December.
    5. Horowitz, Joel L., 2001. "The bootstrap and hypothesis tests in econometrics," Journal of Econometrics, Elsevier, vol. 100(1), pages 37-40, January.
    6. Horowitz, Joel L., 2001. "The Bootstrap," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 52, pages 3159-3228, Elsevier.
    7. 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, June.
    8. 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. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Groneck, Max & Ludwig, Alexander & Zimper, Alexander, 2016. "A life-cycle model with ambiguous survival beliefs," Journal of Economic Theory, Elsevier, vol. 162(C), pages 137-180.
    3. Benjamin Faber & Cecile Gaubert, 2019. "Tourism and Economic Development: Evidence from Mexico's Coastline," American Economic Review, American Economic Association, vol. 109(6), pages 2245-2293, June.
    4. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2016. "Testing for Granger causality in large mixed-frequency VARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 418-432.
    5. Grammig, Joachim & Küchlin, Eva-Maria, 2017. "A two-step indirect inference approach to estimate the long-run risk asset pricing model," CFR Working Papers 17-01, University of Cologne, Centre for Financial Research (CFR).
    6. Puente-Ajovín, Miguel & Sanso-Navarro, Marcos, 2015. "Granger causality between debt and growth: Evidence from OECD countries," International Review of Economics & Finance, Elsevier, vol. 35(C), pages 66-77.
    7. Huang, Zhendong & Ferrari, Davide & Qian, Guoqi, 2017. "Parsimonious and powerful composite likelihood testing for group difference and genotype–phenotype association," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 37-49.
    8. Saqib Aziz & Michael Dowling & Jean-Jacques Lilti, 2016. "Bank Acquisitiveness and Financial Crisis Vulnerability," Post-Print hal-01393953, HAL.
    9. Benjamin Faber & Thibault Fally, 2022. "Firm Heterogeneity in Consumption Baskets: Evidence from Home and Store Scanner Data [Measuring Trends in Leisure: The Allocation of Time over Five Decades]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 89(3), pages 1420-1459.
    10. Hardwick Tchale & Johannes Sauer, 2007. "The efficiency of maize farming in Malawi. A bootstrapped translog frontier," Post-Print hal-01201145, HAL.
    11. Lee, Tae-Hwy & Tu, Yundong & Ullah, Aman, 2014. "Nonparametric and semiparametric regressions subject to monotonicity constraints: Estimation and forecasting," Journal of Econometrics, Elsevier, vol. 182(1), pages 196-210.
    12. Lee, Seojeong, 2016. "Asymptotic refinements of a misspecification-robust bootstrap for GEL estimators," Journal of Econometrics, Elsevier, vol. 192(1), pages 86-104.
    13. Gustavo J. Bobonis & Paul J. Gertler & Marco Gonzalez-Navarro & Simeon Nichter, 2022. "Vulnerability and Clientelism," American Economic Review, American Economic Association, vol. 112(11), pages 3627-3659, November.
    14. Le, Vo Phuong Mai & Meenagh, David & Minford, Patrick & Wickens, Michael, 2011. "How much nominal rigidity is there in the US economy? Testing a new Keynesian DSGE model using indirect inference," Journal of Economic Dynamics and Control, Elsevier, vol. 35(12), pages 2078-2104.
    15. Cziraki, P. & de Goeij, P. C. & Renneboog, L.D.R., 2010. "Insider Trading, Option Exercises and Private Benefits of Control (Revision of DP 2010-32)," Other publications TiSEM d77eb862-1191-40d2-b2e6-f, Tilburg University, School of Economics and Management.
    16. Tchale, Hardwick & Sauer, Johannes, 2007. "The efficiency of maize farming in Malawi. A bootstrapped translog frontier," Cahiers d'Economie et de Sociologie Rurales (CESR), Institut National de la Recherche Agronomique (INRA), vol. 82.
    17. Spierdijk, Laura, 2016. "Confidence intervals for ARMA–GARCH Value-at-Risk: The case of heavy tails and skewness," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 545-559.
    18. Hardwick Tchale & Johannes Sauer, 2007. "The efficiency of maize farming in Malawi. A bootstrapped translog frontier," Cahiers d'Economie et Sociologie Rurales, INRA Department of Economics, vol. 82, pages 33-56.
    19. Shimizu Kenichi, 2013. "The bootstrap does not alwayswork for heteroscedasticmodels," Statistics & Risk Modeling, De Gruyter, vol. 30(3), pages 189-204, August.
    20. Talamakrouni, Majda & Van Keilegom, Ingrid & El Ghouch, Anouar, 2016. "Parametrically guided nonparametric density and hazard estimation with censored data," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 308-323.

    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:eee:intfor:v:28:y:2012:i:1:p:288-296. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.