Ricerc@Sapienza

The last financial crisis emphasized the fact that the consequences of credit risk management could affect not only one financial institution but also the entire financial system and going beyond to the global economy.In response to the last crisis, the Basel Committee on Banking Supervision and the Bank of International Settlement proposed a set of banking regulations referred as Basel II to quantify their capital requirements for credit risk. Loss Given Default (LGD) is one of the main credit risk parameters that has to be carefully evaluated by banks. The challenge of finding an effective forecasting methodology for Loss Given Default as a critical component in credit risk management, is increasing in importance. The improvements in prediction accuracy are essential for lenders and investors for a better assessment of credit risk and pricing strategies while estimating possible future credit losses. Loss Given Default presents the credit loss incurred if a counterparty of a bank defaults and is one of the key elements in the framework of Basel II for calculating banks regulatory capital requirements. Recent studies have emphasized the use of innovative algorithms as an alternative interesting forecasting tool in the LGD topic.The use of these algorithms is a new research area in this field and still remains limited since there are several algorithms that have confirmed to be successful in other fields which can be implemented. In this spirit, this study aims to explore different forecasting algorithms with the aim to improve the accuracy when predicting LGD as a critical component in the credit risk framework.