Modelling Recovery Rates for Non-Performing Loans

Hui Ye and Anthony Bellotti
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Hui Ye: Department of Mathematics, Imperial College London, London SW7 2AZ, UK
Anthony Bellotti: Department of Mathematics, Imperial College London, London SW7 2AZ, UK

Risks, 2019, vol. 7, issue 1, 1-17

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.

Keywords: recovery rates; beta regression; credit risk (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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