Estimating bank default with generalised extreme value regression models

Abstract

The paper proposes a novel model for the prediction of bank failures, on the basis of both macroeconomic and bank-specific microeconomic factors. As bank failures are rare, in the paper we apply a regression method for binary data based on extreme value theory, which turns out to be more effective than classical logistic regression models, as it better leverages the information in the tail of the default distribution. The application of this model to the occurrence of bank defaults in a highly bank dependent economy (Italy) shows that, while microeconomic factors as well as regulatory capital are significant to explain proper failures, macroeconomic factors are relevant only when failures are defined not only in terms of actual defaults but also in terms of mergers and acquisitions. In terms of predictive accuracy, the model based on extreme value theory outperforms classical logistic regression models.

Keywords:

• Credit scoring for banks
• Generalised extreme value distribution
• Camels ratio predictors

Acknowledgements

The authors acknowledge useful comments and discussion at the Paris conference FEBS/LabEx ReFi 2013 and, subsequently, by the referees. The authors also acknowledge financial support from the MIUR PRIN project MISURA: Multivariate statistical models for risk assessment.