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Modelling Recovery Rates for Non-Performing Loans

Author

Listed:
  • Hui Ye

    (Department of Mathematics, Imperial College London, London SW7 2AZ, UK)

  • Anthony Bellotti

    (Department of Mathematics, Imperial College London, London SW7 2AZ, UK)

Abstract

Based on a rich dataset of recoveries donated by a debt collection business, recovery rates for non-performing loans taken from a single European country are modelled using linear regression, linear regression with Lasso, beta regression and inflated beta regression. We also propose a two-stage model: beta mixture model combined with a logistic regression model. The proposed model allowed us to model the multimodal distribution we found for these recovery rates. All models were built using loan characteristics, default data and collections data prior to purchase by the debt collection business. The intended use of the models was to estimate future recovery rates for improved risk assessment, capital requirement calculations and bad debt management. They were compared using a range of quantitative performance measures under K -fold cross validation. Among all the models, we found that the proposed two-stage beta mixture model performs best.

Suggested Citation

  • Hui Ye & Anthony Bellotti, 2019. "Modelling Recovery Rates for Non-Performing Loans," Risks, MDPI, vol. 7(1), pages 1-17, February.
    • Handle: RePEc:gam:jrisks:v:7:y:2019:i:1:p:19-:d:207676
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    References listed on IDEAS

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    Cited by:

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    2. Alexandra Z. Marouli & Eugenia N. Giannini & Yannis D. Caloghirou, 2023. "A Non-Performing Loans (NPLs) Portfolio Pricing Model Based on Recovery Performance: The Case of Greece," Risks, MDPI, vol. 11(5), pages 1-17, May.
    3. Bellotti, Anthony & Brigo, Damiano & Gambetti, Paolo & Vrins, Frédéric, 2021. "Forecasting recovery rates on non-performing loans with machine learning," International Journal of Forecasting, Elsevier, vol. 37(1), pages 428-444.
    4. Giuseppe Orlando & Roberta Pelosi, 2020. "Non-Performing Loans for Italian Companies: When Time Matters. An Empirical Research on Estimating Probability to Default and Loss Given Default," IJFS, MDPI, vol. 8(4), pages 1-22, November.
    5. Michela Pelizza & Klaus R. Schenk-Hoppé, 2020. "Pricing Defaulted Italian Mortgages," JRFM, MDPI, vol. 13(2), pages 1-14, February.
    6. Aleksey Min & Matthias Scherer & Amelie Schischke & Rudi Zagst, 2020. "Modeling Recovery Rates of Small- and Medium-Sized Entities in the US," Mathematics, MDPI, vol. 8(11), pages 1-18, October.
    7. Marc Gürtler & Marvin Zöllner, 2023. "Heterogeneities among credit risk parameter distributions: the modality defines the best estimation method," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 251-287, March.

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