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Loss given default for leasing: Parametric and nonparametric estimations

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

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    Bibliographic Info

    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

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    Web page: http://www.elsevier.com/locate/jbf

    Related research

    Keywords: Loss given default; Regression and model trees; Finite mixture models; Leasing; Forecasting;

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    References

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    1. M.Ameziane Lasfer & Mario Levis, 1998. "The Determinants of the Leasing Decision of Small and Large Companies," European Financial Management, European Financial Management Association, European Financial Management Association, vol. 4(2), pages 159-184.
    2. Joao A. Bastos, 2009. "Forecasting bank loans loss-given-default," CEMAPRE Working Papers, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon 0901, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.
    3. Calabrese, Raffaella & Zenga, Michele, 2010. "Bank loan recovery rates: Measuring and nonparametric density estimation," Journal of Banking & Finance, Elsevier, Elsevier, vol. 34(5), pages 903-911, May.
    4. Olivier RENAULT & Olivier SCAILLET, 2003. "On the Way to Recovery: A Nonparametric Bias Free Estimation of Recovery Rate Densities," FAME Research Paper Series, International Center for Financial Asset Management and Engineering rp83, International Center for Financial Asset Management and Engineering.
    5. Marie-Paule Laurent & Mathias Schmit, 2005. "Estimating distressed LGD on defaulted exposures: a portfolio model applied to leasing contracts," ULB Institutional Repository 2013/14421, ULB -- Universite Libre de Bruxelles.
    6. Loterman, Gert & Brown, Iain & Martens, David & Mues, Christophe & Baesens, Bart, 2012. "Benchmarking regression algorithms for loss given default modeling," International Journal of Forecasting, Elsevier, Elsevier, vol. 28(1), pages 161-170.
    7. Han, Chulwoo & Jang, Youngmin, 2013. "Effects of debt collection practices on loss given default," Journal of Banking & Finance, Elsevier, Elsevier, vol. 37(1), pages 21-31.
    8. Grun, Bettina & Leisch, Friedrich, 2007. "Fitting finite mixtures of generalized linear regressions in R," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 51(11), pages 5247-5252, July.
    9. Gray, J. Brian & Fan, Guangzhe, 2008. "Classification tree analysis using TARGET," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 52(3), pages 1362-1372, January.
    10. Bellotti, Tony & Crook, Jonathan, 2012. "Loss given default models incorporating macroeconomic variables for credit cards," International Journal of Forecasting, Elsevier, Elsevier, vol. 28(1), pages 171-182.
    11. Gürtler, Marc & Hibbeln, Martin, 2013. "Improvements in loss given default forecasts for bank loans," Journal of Banking & Finance, Elsevier, Elsevier, vol. 37(7), pages 2354-2366.
    12. Schmit, Mathias, 2004. "Credit risk in the leasing industry," Journal of Banking & Finance, Elsevier, Elsevier, vol. 28(4), pages 811-833, April.
    13. Zhang, Jie & Thomas, Lyn C., 2012. "Comparisons of linear regression and survival analysis using single and mixture distributions approaches in modelling LGD," International Journal of Forecasting, Elsevier, Elsevier, vol. 28(1), pages 204-215.
    14. Qi, Min & Zhao, Xinlei, 2011. "Comparison of modeling methods for Loss Given Default," Journal of Banking & Finance, Elsevier, Elsevier, vol. 35(11), pages 2842-2855, November.
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