Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/257857 
Erscheinungsjahr: 
2019
Quellenangabe: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 7 [Issue:] 1 [Article No.:] 19 [Publisher:] MDPI [Place:] Basel [Year:] 2019 [Pages:] 1-17
Verlag: 
MDPI, Basel
Zusammenfassung: 
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.
Schlagwörter: 
recovery rates
beta regression
credit risk
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