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Multi-period credit default prediction with time-varying covariates

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  • Orth, Walter
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    Abstract

    In credit default prediction models, the need to deal with time-varying covariates often arises. For instance, in the context of corporate default prediction a typical approach is to estimate a hazard model by regressing the hazard rate on time-varying covariates like balance sheet or stock market variables. If the prediction horizon covers multiple periods, this leads to the problem that the future evolution of these covariates is unknown. Consequently, some authors have proposed a framework that augments the prediction problem by covariate forecasting models. In this paper, we present simple alternatives for multi-period prediction that avoid the burden to specify and estimate a model for the covariate processes. In an application to North American public firms, we show that the proposed models deliver high out-of-sample predictive accuracy.

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    Bibliographic Info

    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 30507.

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    Date of creation: 17 Mar 2011
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    Handle: RePEc:pra:mprapa:30507

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    Keywords: Credit default; multi-period predictions; hazard models; panel data; out-of-sample tests;

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    1. Darrel Duffie & Leandro Saita & Ke Wang, 2005. "Multi-Period Corporate Default Prediction With Stochastic Covariates," CIRJE F-Series CIRJE-F-373, CIRJE, Faculty of Economics, University of Tokyo.
    2. Orth, Walter, 2010. "The predictive accuracy of credit ratings: measurement and statistical inference," Discussion Papers in Statistics and Econometrics 2/10, University of Cologne, Department for Economic and Social Statistics.
    3. Jacobson, Tor & Kindell, Rikard & Lindé, Jesper & Roszbach, Kasper, 2008. "Firm Default and Aggregate Fluctuations," Working Paper Series 226, Sveriges Riksbank (Central Bank of Sweden).
    4. Männasoo, Kadri & Mayes, David G., 2009. "Explaining bank distress in Eastern European transition economies," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 244-253, February.
    5. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2006. "Robust Inference with Multi-way Clustering," NBER Technical Working Papers 0327, National Bureau of Economic Research, Inc.
    6. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-24, January.
    7. Ebnother, Silvan & Vanini, Paolo, 2007. "Credit portfolios: What defines risk horizons and risk measurement?," Journal of Banking & Finance, Elsevier, vol. 31(12), pages 3663-3679, December.
    8. 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.
    9. Hanson, Samuel & Schuermann, Til, 2006. "Confidence intervals for probabilities of default," Journal of Banking & Finance, Elsevier, vol. 30(8), pages 2281-2301, August.
    10. Agarwal, Vineet & Taffler, Richard, 2008. "Comparing the performance of market-based and accounting-based bankruptcy prediction models," Journal of Banking & Finance, Elsevier, vol. 32(8), pages 1541-1551, August.
    11. Carling, Kenneth & Jacobson, Tor & Linde, Jesper & Roszbach, Kasper, 2007. "Corporate credit risk modeling and the macroeconomy," Journal of Banking & Finance, Elsevier, vol. 31(3), pages 845-868, March.
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