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Loss given default for leasing: Parametric and nonparametric estimations


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


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.

Suggested Citation

  • Hartmann-Wendels, Thomas & Miller, Patrick & Töws, Eugen, 2014. "Loss given default for leasing: Parametric and nonparametric estimations," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 364-375.
  • Handle: RePEc:eee:jbfina:v:40:y:2014:i:c:p:364-375
    DOI: 10.1016/j.jbankfin.2013.12.006

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    References listed on IDEAS

    1. 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.
    2. Gürtler, Marc & Hibbeln, Martin, 2013. "Improvements in loss given default forecasts for bank loans," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2354-2366.
    3. Bastos, João A., 2010. "Forecasting bank loans loss-given-default," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2510-2517, October.
    4. Qi, Min & Zhao, Xinlei, 2011. "Comparison of modeling methods for Loss Given Default," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2842-2855, November.
    5. Renault, Olivier & Scaillet, Olivier, 2004. "On the way to recovery: A nonparametric bias free estimation of recovery rate densities," Journal of Banking & Finance, Elsevier, vol. 28(12), pages 2915-2931, December.
    6. Calabrese, Raffaella & Zenga, Michele, 2010. "Bank loan recovery rates: Measuring and nonparametric density estimation," Journal of Banking & Finance, Elsevier, vol. 34(5), pages 903-911, May.
    7. M.Ameziane Lasfer & Mario Levis, 1998. "The Determinants of the Leasing Decision of Small and Large Companies," European Financial Management, European Financial Management Association, vol. 4(2), pages 159-184.
    8. Gray, J. Brian & Fan, Guangzhe, 2008. "Classification tree analysis using TARGET," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1362-1372, January.
    9. Loterman, Gert & Brown, Iain & Martens, David & Mues, Christophe & Baesens, Bart, 2012. "Benchmarking regression algorithms for loss given default modeling," International Journal of Forecasting, Elsevier, vol. 28(1), pages 161-170.
    10. Grun, Bettina & Leisch, Friedrich, 2007. "Fitting finite mixtures of generalized linear regressions in R," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5247-5252, July.
    11. Han, Chulwoo & Jang, Youngmin, 2013. "Effects of debt collection practices on loss given default," Journal of Banking & Finance, Elsevier, vol. 37(1), pages 21-31.
    12. Bellotti, Tony & Crook, Jonathan, 2012. "Loss given default models incorporating macroeconomic variables for credit cards," International Journal of Forecasting, Elsevier, vol. 28(1), pages 171-182.
    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, vol. 28(1), pages 204-215.
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    Cited by:

    1. Christophe Hurlin & Jérémy Leymarie & Antoine Patin, 2018. "Loss functions for LGD model comparison," Working Papers halshs-01516147, HAL.
    2. repec:eee:ejores:v:262:y:2017:i:2:p:780-791 is not listed on IDEAS
    3. repec:eee:jbfina:v:96:y:2018:i:c:p:1-17 is not listed on IDEAS
    4. repec:wly:japmet:v:33:y:2018:i:4:p:533-552 is not listed on IDEAS
    5. repec:eee:jbfina:v:91:y:2018:i:c:p:176-188 is not listed on IDEAS
    6. repec:taf:oabmxx:v:3:y:2016:i:1:p:1153864 is not listed on IDEAS
    7. repec:eee:jbfina:v:91:y:2018:i:c:p:189-201 is not listed on IDEAS
    8. repec:taf:acceur:v:15:y:2018:i:1:p:105-133 is not listed on IDEAS
    9. repec:eee:ejores:v:268:y:2018:i:1:p:348-360 is not listed on IDEAS
    10. Bart Keijsers & Bart Diris & Erik Kole, 2018. "Cyclicality in losses on bank loans," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(4), pages 533-552, June.
    11. repec:eee:jbfina:v:89:y:2018:i:c:p:14-25 is not listed on IDEAS
    12. Abdelkader Derbali & Slaheddine Hallara & David McMillan, 2016. "Measuring systemic risk of Greek banks: New approach by using the epidemic model “SEIR”," Cogent Business & Management, Taylor & Francis Journals, vol. 3(1), pages 1153864-115, December.

    More about this item


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

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation


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