Improving Realized LGD Approximation: A Novel Framework with XGBoost for Handling Missing Cash-Flow Data

TL;DR

This paper introduces an XGBoost machine learning framework to improve realized LGD approximation in mortgage portfolios, especially when cash-flow data is limited, demonstrating enhanced accuracy over traditional methods.

Contribution

The paper presents a novel application of XGBoost to estimate LGD without cash-flow data, addressing data limitations and improving accuracy over existing approaches.

Findings

XGBoost model outperforms delta outstanding approach in LGD estimation.

Inclusion of macroeconomic and non-financial variables improves model accuracy.

Method demonstrates general applicability beyond specific legal or portfolio contexts.

Abstract

The scope for the accurate calculation of the Loss Given Default (LGD) parameter is comprehensive in terms of financial data. In this research, we aim to explore methods for improving the approximation of realized LGD in conditions of limited access to the cash-flow data. We enhance the performance of the method which relies on the differences between exposure values (delta outstanding approach) by employing machine learning (ML) techniques. The research utilizes the data from the mortgage portfolio of one of the European countries and assumes a close resemblance to similar economic contexts. It incorporates non-financial variables and macroeconomic data related to the housing market, improving the accuracy of loss severity approximation. The proposed methodology attempts to mitigate the country-specific (related to the local legal) or portfolio-specific factors in aim to show the general advantage of applying ML techniques, rather than case-specific relation. We developed an XGBoost model that does not rely on cash-flow data yet enhances the accuracy of realized LGD estimation compared to results obtained with the delta outstanding approach. A novel aspect of our work is the detailed exploration of the delta outstanding approach and the methodology for addressing conditions of limited access to cash-flow data through machine learning models.