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Modelling Recovery Rates for Non-Performing Loans

Department of Mathematics, Imperial College London, London SW7 2AZ, UK
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Risks 2019, 7(1), 19; https://doi.org/10.3390/risks7010019
Received: 12 February 2019 / Accepted: 15 February 2019 / Published: 20 February 2019
(This article belongs to the Special Issue Advances in Credit Risk Modeling and Management)
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. View Full-Text
Keywords: recovery rates; beta regression; credit risk recovery rates; beta regression; credit risk
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Ye, H.; Bellotti, A. Modelling Recovery Rates for Non-Performing Loans. Risks 2019, 7, 19.

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