Variable Selection in Estimating Bank Default

The crisis of the first decade of the 21st century has definitely changed the approaches used to analyze data originated from financial markets. This break and the growing availability of information have lead to revise the methodologies traditionally used to model and evaluate phenomena related to financial institutions. In this context we focus the attention on the estimation of bank defaults: a large literature has been proposed to model the binary dependent variable that characterizes this empirical domain and promising results have been obtained from the application of regression methods based on the extreme value theory. In this context we consider, as dependent variable, a strongly asymmetric binary variable whose probabilistic structure can be related to the Generalized Extreme Value (GEV) distribution. Further we propose to select the independent variables through proper penalty procedures and appropriate data screenings that could be of great interest in presence of large datasets.