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

Paper: arXiv 2406.17308

Authors: Zuzanna Kostecka, Robert Ślepaczuk

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.

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 6.5/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper tackles a practical credit risk issue by using XGBoost to approximate realized LGD when cash-flow data is missing. It relies primarily on empirical machine learning techniques and real-world mortgage portfolio data rather than dense mathematical derivations.

Research Flowchart

  flowchart TD
    A["Research Goal: Improve Realized LGD Approximation with Limited Cash-Flow Data"] --> B["Data Collection: Mortgage Portfolio, Non-Financial & Housing Macro Data"]
    B --> C["Baseline Method: Delta Outstanding Approach (Exposure Differences)"]
    C --> D["Machine Learning Framework: Train XGBoost Model without Cash-Flow Data"]
    D --> E["Outcome: Enhanced Realized LGD Accuracy Over Baseline Delta Approach"]