Multi-period credit default prediction with time-varying covariates
AbstractIn 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 InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 30507.
Date of creation: 17 Mar 2011
Date of revision:
Credit default; multi-period predictions; hazard models; panel data; out-of-sample tests;
Find related papers by JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
- G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-05-07 (All new papers)
- NEP-ECM-2011-05-07 (Econometrics)
- NEP-FOR-2011-05-07 (Forecasting)
- NEP-RMG-2011-05-07 (Risk Management)
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