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.
Keywords
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
Publisher
:Emerald Group Publishing Limited
Copyright © 2014, Emerald Group Publishing Limited