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Comparison of modeling methods for Loss Given Default

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  • Qi, Min
  • Zhao, Xinlei

Abstract

We compare six modeling methods for Loss Given Default (LGD). We find that non-parametric methods (regression tree and neural network) perform better than parametric methods both in and out of sample when over-fitting is properly controlled. Among the parametric methods, fractional response regression has a slight edge over OLS regression. Performance of the transformation methods (inverse Gaussian and beta transformation) is very sensitive to [epsilon], a small adjustment made to LGDs of 0 or 1 prior to transformation. Model fit is poor when [epsilon] is too small or too large, although the fitted LGDs have strong bi-modal distribution with very small [epsilon]. Therefore, models that produce strong bi-model pattern do not necessarily have good model fit and accurate LGD predictions. Even with an optimal [epsilon], the performance of the transformation methods can only match that of the OLS.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:jbfina:v:35:y:2011:i:11:p:2842-2855
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    Cited by:

    1. Nithi Sopitpongstorn & Param Silvapulle & Jiti Gao, 2017. "Local logit regression for recovery rate," Monash Econometrics and Business Statistics Working Papers 19/17, Monash University, Department of Econometrics and Business Statistics.
    2. Yao, Xiao & Crook, Jonathan & Andreeva, Galina, 2015. "Support vector regression for loss given default modelling," European Journal of Operational Research, Elsevier, vol. 240(2), pages 528-538.
    3. repec:eee:ejores:v:262:y:2017:i:2:p:780-791 is not listed on IDEAS
    4. Agata M. Lozinskaia & Evgeniy M. Ozhegov & Alexander M. Karminsky, 2016. "Discontinuity in Relative Credit Losses: Evidence from Defaults on Government-Insured Residential Mortgages," HSE Working papers WP BRP 55/FE/2016, National Research University Higher School of Economics.
    5. Azusa Takeyama & Nick Constantinou & Dmitri Vinogradov, 2012. "A Framework for Extracting the Probability of Default from Stock Option Prices," IMES Discussion Paper Series 12-E-14, Institute for Monetary and Economic Studies, Bank of Japan.
    6. Yao, Xiao & Crook, Jonathan & Andreeva, Galina, 2017. "Is it obligor or instrument that explains recovery rate: Evidence from US corporate bond," Journal of Financial Stability, Elsevier, vol. 28(C), pages 1-15.
    7. repec:spr:compst:v:33:y:2018:i:1:d:10.1007_s00180-017-0747-x is not listed on IDEAS
    8. Andersson, Fredrik & Mayock, Tom, 2014. "Loss severities on residential real estate debt during the Great Recession," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 266-284.
    9. Wei, Li & Yuan, Zhongyi, 2016. "The loss given default of a low-default portfolio with weak contagion," Insurance: Mathematics and Economics, Elsevier, vol. 66(C), pages 113-123.
    10. Betz, Jennifer & Kellner, Ralf & Rösch, Daniel, 2016. "What drives the time to resolution of defaulted bank loans?," Finance Research Letters, Elsevier, vol. 18(C), pages 7-31.
    11. Lozinskaia Agata & Ozhegov Evgeniy, 2016. "Key Determinants of Demand, Credit Underwriting, and Performance on Government-Insured Mortgage Loans in Russia," EERC Working Paper Series 16/03e, EERC Research Network, Russia and CIS.
    12. repec:eee:ejores:v:263:y:2017:i:2:p:679-689 is not listed on IDEAS
    13. Christophe Hurlin & Jérémy Leymarie & Antoine Patin, 2018. "Loss functions for LGD model comparison," Working Papers halshs-01516147, HAL.
    14. Tong, Edward N.C. & Mues, Christophe & Thomas, Lyn, 2013. "A zero-adjusted gamma model for mortgage loan loss given default," International Journal of Forecasting, Elsevier, vol. 29(4), pages 548-562.
    15. Hussain, Inayat & Durand, Robert B. & Harris, Mark N., 2016. "Default resolution and access to fresh credit in an emerging market," Pacific-Basin Finance Journal, Elsevier, vol. 39(C), pages 256-274.
    16. Francesca Castelli & Damien Moore & Gabriel Ehrlich & Jeffrey Perry, 2014. "Modeling the Budgetary Costs of FHA's Single Family Mortgage Insurance: Working Paper 2014-05," Working Papers 45711, Congressional Budget Office.
    17. Ellen Tobback & David Martens & Tony Van Gestel & Bart Baesens, 2014. "Forecasting Loss Given Default models: impact of account characteristics and the macroeconomic state," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 376-392, March.
    18. repec:eee:jbfina:v:79:y:2017:i:c:p:42-56 is not listed on IDEAS
    19. Ruey-Ching Hwang & Huimin Chung & C. K. Chu, 2016. "A Two-Stage Probit Model for Predicting Recovery Rates," Journal of Financial Services Research, Springer;Western Finance Association, vol. 50(3), pages 311-339, December.
    20. Altman, Edward I. & Kalotay, Egon A., 2014. "Ultimate recovery mixtures," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 116-129.
    21. 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.
    22. Tong, Edward N.C. & Mues, Christophe & Brown, Iain & Thomas, Lyn C., 2016. "Exposure at default models with and without the credit conversion factor," European Journal of Operational Research, Elsevier, vol. 252(3), pages 910-920.
    23. Konstantin Belyaev & Aelita Belyaeva & Tomas Konecny & Jakub Seidler & Martin Vojtek, 2012. "Macroeconomic Factors as Drivers of LGD Prediction: Empirical Evidence from the Czech Republic," Working Papers 2012/12, Czech National Bank, Research Department.
    24. 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.

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