Estimation of Loss Given Default Distributions for Non-Performing Loans Using Zero-and-One Inflated Beta Regression Type Models

Abstract: This thesis investigates three different techniques for estimating loss given default of non-performing consumer loans. This is a contribution to a credit risk evaluation model compliant with the regulations stipulated by the Basel Accords, regulating the capital requirements of European financial institutions. First, multiple linear regression is applied, and thereafter, zero-and-one inflated beta regression is implemented in two versions, with and without Bayesian inference. The model performances confirm that modeling loss given default data is challenging, however, the result shows that the zero-and-one inflated beta regression is superior to the other models in predicting LGD. Although, it shall be recognized that all models had difficulties in distinguishing low-risk loans, while the prediction accuracy of riskier loans, resulting in larger losses, were higher. It is further recommended, in future research, to include macroeconomic variables in the models to capture economic downturn conditions as well as adopting decision trees, for example by applying machine learning.