the web's biggest credit risk modeling resource.

Credit Jobs

Home Glossary Links FAQ / About Site Guide Search


Submit Your Paper

In Rememberance: World Trade Center (WTC)

Export citation to:
- Text (plain)
- BibTeX

Modelling Small and Medium Enterprise Loan Defaults as Rare Events: The generalized extreme value regression model

by Raffaella Calabrese of University of Milano-Bicocca, and
Silvia Angela Osmetti of University Cattolica del Sacro Cuore, Milan


Abstract: A pivotal characteristic of credit defaults that is ignored by most credit scoring models is the rarity of the event. The most widely used model to estimate the probability of default is the logistic regression model. Since the dependent variable represents a rare event, the logistic regression model shows relevant drawbacks, for example, underestimation of the default probability, which could be very risky for banks. In order to overcome these drawbacks, we propose the generalized extreme value regression model. In particular, in a generalized linear model (GLM) with the binary-dependent variable we suggest the quantile function of the GEV distribution as link function, so our attention is focused on the tail of the response curve for values close to one. The estimation procedure used is the maximum-likelihood method. This model accommodates skewness and it presents a generalisation of GLMs with complementary log-log link function. We analyse its performance by simulation studies. Finally, we apply the proposed model to empirical data on Italian small and medium enterprises.

Keywords: credit defaults, small and medium enterprises, generalized linear model, generalized extreme value distribution, rare events, binary data.

Published in: Journal of Applied Statistics, Vol. 40, No. 6, (2013), pp. 1172-1188.

Previously titled: Generalized Extreme Value for Binary Rare Events Data: An application to credit defaults

Books Referenced in this paper:  (what is this?)

Download paper (237K PDF) 20 pages

Most Cited Books within Credit Modeling Papers