Models for predicting default: towards efficient forecasts

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.

Citation

Castagnolo, F. and Ferro, G. (2014), "Models for predicting default: towards efficient forecasts", Journal of Risk Finance, Vol. 15 No. 1, pp. 52-70. https://doi.org/10.1108/JRF-08-2013-0057