TY  - JOUR
AB  - 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.
VL  - 15
IS  - 1
SN  - 1526-5943
DO  - 10.1108/JRF-08-2013-0057
UR  - https://doi.org/10.1108/JRF-08-2013-0057
AU  - Castagnolo Fernando
AU  - Ferro Gustavo
PY  - 2014
Y1  - 2014/01/01
TI  - Models for predicting default: towards efficient forecasts
T2  - The Journal of Risk Finance
PB  - Emerald Group Publishing Limited
SP  - 52
EP  - 70
Y2  - 2024/04/24
ER  -