A Dual-Lens Approach to Loss Given Default Estimation: Traditional Methods and Variable Analysis

Abstract: This report seeks to thoroughly examine different approaches to estimating Loss Given Default through a comparison of traditional estimation methods, as well as a deeper variable analysis on micro, small, and medium-sized companies using primarily regression decision trees. The comparative study concluded that estimating loss given default depends heavily on business-specific factors and data variety. While regression models offer interpretability and machine learning techniques offer superior prediction, model selection should balance complexity, computational demands, implementation ease, and overall performance. From the variable analysis, loan size and guarantor property ownership emerged as key drivers for a lower Loss Given Default.