INVESTIGADORES
FERRO Gustavo Adolfo
artículos
Título:
Models for predicting default: towards efficient forecasts
Autor/es:
FERNANDO CASTAGNOLO Y GUSTAVO FERRO
Revista:
Journal of Risk Finance
Editorial:
Emerald Group Publishers
Referencias:
Lugar: Londres; Año: 2014 vol. 15 p. 52 - 70
ISSN:
1526-5943
Resumen:
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
Paper type Research paper