Models for predicting default: towards efficient forecasts

Fernando Castagnolo and Gustavo Ferro

Journal of Risk Finance, 2014, vol. 15, issue 1, 52-70

Abstract: Purpose - – The purpose of this paper is to assess and compare the forecast ability of existing credit risk models, answering three questions: Can these methods adequately predict default events? Are there dominant methods? Is it safer to rely on a mix of methodologies? Design/methodology/approach - – The authors examine four existing models: O-score, Z-score, Campbell, and Merton distance to default model (MDDM). The authors compare their ability to forecast defaults using three techniques: intra-cohort analysis, power curves and discrete hazard rate models. Findings - – The authors conclude that better predictions demand a mix of models containing accounting and market information. The authors found evidence of the O-score's outperformance relative to the other models. The MDDM alone in the sample is not a sufficient default predictor. But discrete hazard rate models suggest that combining both should enhance default prediction models. Research limitations/implications - – The analysed methods alone cannot adequately predict defaults. The authors found no dominant methods. Instead, it would be advisable to rely on a mix of methodologies, which use complementary information. Practical implications - – Better forecasts demand a mix of models containing both accounting and market information. Originality/value - – The findings suggest that more precise default prediction models can be built by combining information from different sources in reduced-form models and combining default prediction models that can analyze said information.

Keywords: Financial crisis; Efficiency; Credit risk; Empirical analysis; Predicting default models (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:eme:jrfpps:jrf-08-2013-0057

DOI: 10.1108/JRF-08-2013-0057

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