Modelling Recovery Rates for Non-performing Loans

22 Pages Posted: 20 Oct 2018

See all articles by Anthony Bellotti

Anthony Bellotti

University of Nottingham Ningbo China

Hui Ye

Imperial College London - Department of Mathematics

Date Written: September 27, 2018

Abstract

Based on a rich data set 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 allows us to model the multimodal distribution we find in these recovery rates. All models are built using loan characteristics, default data and collections data prior to purchase by the debt collection business. The intended use of the models is to estimate future recovery rates for improved risk assessment, capital requirement calculations and bad debt management. They are compared using a range of quantitative performance measures under K-fold cross validation. Among all the models, we find that the proposed two-stage beta mixture model performs best.

Keywords: LGD recovery, non-performing loan, beta regression

JEL Classification: C10, G20

Suggested Citation

Bellotti, Anthony and Ye, Hui, Modelling Recovery Rates for Non-performing Loans (September 27, 2018). Available at SSRN: https://ssrn.com/abstract=3256252 or http://dx.doi.org/10.2139/ssrn.3256252

Anthony Bellotti (Contact Author)

University of Nottingham Ningbo China ( email )

199 Taikang East Road
Yinzhou
Ningbo, Zhejiang 315100
China
+1008619883003047 (Phone)

Hui Ye

Imperial College London - Department of Mathematics ( email )

South Kensington Campus
Imperial College
LONDON, SW7 2AZ
United Kingdom

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