Ricerc@Sapienza

This study analyzes different parametric and non-parametric modeling methods for estimating the Loss Given Default (LGD) of bank loans for shipping finance. The shipping industry is considered the backbone of global trade and the global economy but it is associated with several risks which create the need for a more detailed loss modeling from the bank perspective. LGD is the amount of money a financial institution loses when a borrower defaults on a loan, expressed as a percentage of total exposure. For this study, we will use a unique database of defaulted loans in European banks that are involved in shipping finance. The main goal of this study is twofold: to compare the performance of alternative LGD modeling methodologies in shipping finance and to provide some insights into what drives LGD in the shipping industry. To achieve this, the research study will be divided into two main parts. First, we will compare the performances of traditional statistical parametric models with a wide set of machine learning algorithms including bagged trees, random forest, boosted trees, support vector machines, and multivariate adaptive regression splines (MARS). Secondly, we will apply a variable importance measure built on the idea of the permutation importance, to analyze the risk drivers with the greatest effects on the LGD for shipping finance prediction accuracy for each method. In this regard, we further explore what features drive the results of each algorithm's prediction. Therefore, in this way, we first identify the best forecasting method in the shipping-related transactions, as well well go beyond this and throw some light on the popular perception of the "black box" nature of machine learning decisions.