Improving Realized LGD approximation: A Novel Framework with XGBoost for handling missing cash-flow data

Zuzanna Kostecka and Robert Ślepaczuk
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Zuzanna Kostecka: University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group

No 2024-12, Working Papers from Faculty of Economic Sciences, University of Warsaw

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 the machine learning (ML) techniques. The research utilizes the data from the mortgage portfolio of one of the European countries and assumes the close resemblance for 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.

Keywords: LGD; Credit risk; Outstanding; Machine Learning; Missing data; Mortgage loan; financial statements; macroeconomic data (search for similar items in EconPapers)
JEL-codes: C4 C45 C55 C65 G11 (search for similar items in EconPapers)
Pages: 40 pages
Date: 2024
New Economics Papers: this item is included in nep-ban and nep-big
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https://www.wne.uw.edu.pl/download_file/4362/0 First version, 2024 (application/pdf)

Related works:
Working Paper: Improving Realized LGD Approximation: A Novel Framework with XGBoost for Handling Missing Cash-Flow Data (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2024-12

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