Default risk and discriminant methodologies for SME

This chapter aims at a better understanding of the effectiveness and efficiency of methodologies that estimate default risk, for both general and industry specific models. The main research question is “how is the probability of default efficiently estimated according to Basel II?” This implies testing for the best methodology to discriminate between “in bonis” and “in default” companies within the Basel II concept of “default”. Results based on a parity sample of 300 + 300 Italian companies show that Multivariate Discriminant Analysis (MDA) or Discriminant Analysis by Generalized Estimating Equations approach (DA-GEE) and Logit Regression (LR) provide better estimation of Default Risk than can Partial Least Squared Regression Discriminant Analysis (PLS-DA). Using PLS-DA, 42% of these companies fall into the overlapping area (grey zone); using DA-GEE and LR, respectively, only 31% and 29% of companies are misclassified. Industry-specific models offer a more efficient classification of the default when compared to the general model, but percentage of errors are not significantly lower.