Research Repository

Estimating bank default with generalised extreme value regression models

Calabrese, Raffaella and Giudici, Paolo (2015) 'Estimating bank default with generalised extreme value regression models.' Journal of the Operational Research Society, 66 (11). pp. 1783-1792. ISSN 0160-5682

JORS.pdf - Accepted Version

Download (317kB) | Preview


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.

Item Type: Article
Uncontrolled Keywords: Credit scoring for banks, generalised extreme value distribution, camels ratio predictors
Subjects: H Social Sciences > HG Finance
Divisions: Faculty of Social Sciences > Essex Business School
Faculty of Social Sciences > Essex Business School > Essex Finance Centre
Depositing User: Clare Chatfield
Date Deposited: 06 Jul 2015 12:19
Last Modified: 05 Sep 2016 01:00

Actions (login required)

View Item View Item