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Survival Analysis in LGD Modeling

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Abstract

The paper proposes an application of the survival time analysis methodology to estimations of the Loss Given Default (LGD) parameter. The main advantage of the survival analysis approach compared to classical regression methods is that it allows exploiting partial recovery data. The model is also modified in order to improve performance of the appropriate goodness of fit measures. The empirical testing shows that the Cox proportional model applied to LGD modeling performs better than the linear and logistic regressions. In addition a significant improvement is achieved with the modified “pseudo” Cox LGD model.

Suggested Citation

  • Jiří Witzany & Michal Rychnovský & Pavel Charamza, 2010. "Survival Analysis in LGD Modeling," Working Papers IES 2010/02, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Feb 2010.
    • Handle: RePEc:fau:wpaper:wp2010_02
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    Cited by:

    1. Jennifer Betz & Ralf Kellner & Daniel Rösch, 2021. "Time matters: How default resolution times impact final loss rates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 619-644, June.
    2. Matuszyk, Anna & So, Mee Chi & Mues, Christophe & Moore, Angela, 2016. "Modelling repayment patterns in the collections process for unsecured consumer debt: A case studyAuthor-Name: Thomas, Lyn C," European Journal of Operational Research, Elsevier, vol. 249(2), pages 476-486.
    3. Cheng, Dan & Cirillo, Pasquale, 2018. "A reinforced urn process modeling of recovery rates and recovery times," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 1-17.
    4. Jiří Witzany & Anastasiia Kozina, 2022. "Recovery process optimization using survival regression," Operational Research, Springer, vol. 22(5), pages 5269-5296, November.
    5. Ekaterina Rumyantseva & Kirill Furmanov, 2017. "Realisation of mortgage property: Survival analysis," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 48, pages 22-43.
    6. Carleo, Alessandra & Rocci, Roberto, 2024. "Functional clustering of NPLs recovery curves," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    7. Aneta Ptak-Chmielewska & Paweł Kopciuszewski & Anna Matuszyk, 2023. "Application of the kNN-Based Method and Survival Approach in Estimating Loss Given Default for Unresolved Cases," Risks, MDPI, vol. 11(2), pages 1-14, February.
    8. Morne Joubert & Tanja Verster & Helgard Raubenheimer & Willem D. Schutte, 2021. "Adapting the Default Weighted Survival Analysis Modelling Approach to Model IFRS 9 LGD," Risks, MDPI, vol. 9(6), pages 1-17, June.
    9. Joseph L. Breeden, 2024. "An Age–Period–Cohort Framework for Profit and Profit Volatility Modeling," Mathematics, MDPI, vol. 12(10), pages 1-23, May.
    10. Cheng, Hui & Jiang, Cuiqing & Wang, Zhao & Ni, Xiaoya, 2025. "Multi-view locally weighted regression for loss given default forecasting," International Journal of Forecasting, Elsevier, vol. 41(1), pages 290-306.
    11. Arno Botha & Tanja Verster & Roelinde Bester, 2024. "The TruEnd-procedure: Treating trailing zero-valued balances in credit data," Papers 2404.17008, arXiv.org, revised Nov 2025.
    12. Konstantin Belyaev & Aelita Belyaeva & Tomas Konecny & Jakub Seidler & Martin Vojtek, 2012. "Macroeconomic Factors as Drivers of LGD Prediction: Empirical Evidence from the Czech Republic," Working Papers 2012/12, Czech National Bank, Research and Statistics Department.

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    Keywords

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    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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