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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 of Cologne, Department for Economic and Social Statistics in its series Discussion Papers in Statistics and Econometrics with number 3/11.

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    Date of creation: 2011
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    Handle: RePEc:zbw:ucdpse:311

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    Related research

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

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