Loss given default for leasing: Parametric and nonparametric estimations

Thomas Hartmann-Wendels, Patrick Miller and Eugen Töws

Journal of Banking & Finance, 2014, vol. 40, issue C, 364-375

Abstract: This study employs a dataset from three German leasing companies with 14,322 defaulted leasing contracts to analyze different approaches to estimating the loss given default (LGD). Using the historical average LGD and simple OLS-regression as benchmarks, we compare hybrid finite mixture models (FMMs), model trees and regression trees and we calculate the mean absolute error, root mean squared error, and the Theil inequality coefficient. The relative estimation accuracy of the methods depends, among other things, on the number of observations and whether in-sample or out-of-sample estimations are considered. The latter is decisive for proper risk management and is required for regulatory purposes. FMMs aim to reproduce the distribution of realized LGDs and, therefore, perform best with respect to in-sample estimations, but they show poor performance with respect to out-of-sample estimations. Model trees, by contrast, are more robust and outperform all other methods if the sample size is sufficiently large.

Keywords: Loss given default; Regression and model trees; Finite mixture models; Leasing; Forecasting (search for similar items in EconPapers)
JEL-codes: C14 C38 C51 G17 G28 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (29)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:40:y:2014:i:c:p:364-375

DOI: 10.1016/j.jbankfin.2013.12.006

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