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Loss given default for leasing: Parametric and nonparametric estimations

  • Hartmann-Wendels, Thomas
  • Miller, Patrick
  • Töws, Eugen
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    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.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0378426613004688
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    Article provided by Elsevier in its journal Journal of Banking & Finance.

    Volume (Year): 40 (2014)
    Issue (Month): C ()
    Pages: 364-375

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    Handle: RePEc:eee:jbfina:v:40:y:2014:i:c:p:364-375
    Contact details of provider: Web page: http://www.elsevier.com/locate/jbf

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    1. Olivier RENAULT & Olivier SCAILLET, 2003. "On the Way to Recovery: A Nonparametric Bias Free Estimation of Recovery Rate Densities," FAME Research Paper Series rp83, International Center for Financial Asset Management and Engineering.
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