Multi-period credit default prediction with time-varying covariates

Walter Orth

No 3/11, Discussion Papers in Econometrics and Statistics from University of Cologne, Institute of Econometrics and Statistics

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.

Keywords: credit default; multi-period predictions; hazard models; panel data; out-of-sample tests (search for similar items in EconPapers)
JEL-codes: C41 C53 C58 G17 G32 G33 (search for similar items in EconPapers)
Date: 2011
New Economics Papers: this item is included in nep-for and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:ucdpse:311

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