Modeling Recovery Rates of Small- and Medium-Sized Entities in the US

Aleksey Min, Matthias Scherer, Amelie Schischke and Rudi Zagst
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Aleksey Min: Department of Mathematics, Technical University of Munich, Boltzmannstr. 3, 85748 Garching, Germany
Matthias Scherer: Department of Mathematics, Technical University of Munich, Boltzmannstr. 3, 85748 Garching, Germany
Amelie Schischke: Department of Mathematics, Technical University of Munich, Boltzmannstr. 3, 85748 Garching, Germany
Rudi Zagst: Department of Mathematics, Technical University of Munich, Boltzmannstr. 3, 85748 Garching, Germany

Mathematics, 2020, vol. 8, issue 11, 1-18

Abstract: A sound statistical model for recovery rates is required for various applications in quantitative risk management, with the computation of capital requirements for loan portfolios as one important example. We compare different models for predicting the recovery rate on borrower level including linear and quantile regressions, decision trees, neural networks, and mixture regression models. We fit and apply these models on the worldwide largest loss and recovery data set for commercial loans provided by GCD, where we focus on small- and medium-sized entities in the US. Additionally, we include macroeconomic information via a predictive Crisis Indicator or Crisis Probability indicating whether economic downturn scenarios are expected within the time of resolution. The horserace is won by the mixture regression model which regresses the densities as well as the probabilities that an observation belongs to a certain component.

Keywords: decision tree; loss given default; mixture model; neural network; predictive crisis indicator (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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