Multi-Period Credit Default Prediction with Time-Varying Covariates

Discussion Papers in Statistics and Econometrics No. 3/11

18 Pages Posted: 24 Mar 2011 Last revised: 22 Nov 2011

See all articles by Walter Orth

Walter Orth

University of Cologne - Department of Statistics and Econometrics

Date Written: November 22, 2011

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.

Suggested Citation

Orth, Walter, Multi-Period Credit Default Prediction with Time-Varying Covariates (November 22, 2011). Discussion Papers in Statistics and Econometrics No. 3/11, Available at SSRN: https://ssrn.com/abstract=1788826 or http://dx.doi.org/10.2139/ssrn.1788826

Walter Orth (Contact Author)

University of Cologne - Department of Statistics and Econometrics ( email )

Albertus-Magnus-Platz
Cologne, DE 50923
Germany
(0049) 0221-470-6561 (Phone)

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