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Support vector regression for loss given default modelling

Author

Listed:
  • Yao, Xiao
  • Crook, Jonathan
  • Andreeva, Galina

Abstract

Loss given default modelling has become crucially important for banks due to the requirement that they comply with the Basel Accords and to their internal computations of economic capital. In this paper, support vector regression (SVR) techniques are applied to predict loss given default of corporate bonds, where improvements are proposed to increase prediction accuracy by modifying the SVR algorithm to account for heterogeneity of bond seniorities. We compare the predictions from SVR techniques with thirteen other algorithms. Our paper has three important results. First, at an aggregated level, the proposed improved versions of support vector regression techniques outperform other methods significantly. Second, at a segmented level, by bond seniority, least square support vector regression demonstrates significantly better predictive abilities compared with the other statistical models. Third, standard transformations of loss given default do not improve prediction accuracy. Overall our empirical results show that support vector regression techniques are a promising technique for banks to use to predict loss given default.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ejores:v:240:y:2015:i:2:p:528-538
    DOI: 10.1016/j.ejor.2014.06.043
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    References listed on IDEAS

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    1. Bastos, João A., 2010. "Forecasting bank loans loss-given-default," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2510-2517, October.
    2. 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.
    3. Qi, Min & Yang, Xiaolong, 2009. "Loss given default of high loan-to-value residential mortgages," Journal of Banking & Finance, Elsevier, vol. 33(5), pages 788-799, May.
    4. Acharya, Viral V. & Bharath, Sreedhar T. & Srinivasan, Anand, 2007. "Does industry-wide distress affect defaulted firms? Evidence from creditor recoveries," Journal of Financial Economics, Elsevier, vol. 85(3), pages 787-821, September.
    5. Khieu, Hinh D. & Mullineaux, Donald J. & Yi, Ha-Chin, 2012. "The determinants of bank loan recovery rates," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 923-933.
    6. 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.
    7. Papke, Leslie E & Wooldridge, Jeffrey M, 1996. "Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 619-632, Nov.-Dec..
    8. 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.
    9. Dermine, J. & de Carvalho, C. Neto, 2006. "Bank loan losses-given-default: A case study," Journal of Banking & Finance, Elsevier, vol. 30(4), pages 1219-1243, April.
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    Citations

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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. Christophe Hurlin & Jérémy Leymarie & Antoine Patin, 2018. "Loss functions for LGD model comparison," Working Papers halshs-01516147, HAL.
    3. repec:eee:ejores:v:262:y:2017:i:2:p:780-791 is not listed on IDEAS
    4. repec:rfe:zbefri:v:37:y:2019:i:1:p:139-172 is not listed on IDEAS
    5. repec:eee:ejores:v:271:y:2018:i:3:p:1113-1144 is not listed on IDEAS
    6. repec:eee:ejores:v:276:y:2019:i:2:p:710-722 is not listed on IDEAS
    7. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    8. repec:eee:jbfina:v:91:y:2018:i:c:p:189-201 is not listed on IDEAS
    9. repec:eee:ejores:v:271:y:2018:i:2:p:664-675 is not listed on IDEAS
    10. repec:eee:ejores:v:263:y:2017:i:2:p:679-689 is not listed on IDEAS
    11. repec:spr:annopr:v:266:y:2018:i:1:d:10.1007_s10479-017-2668-z is not listed on IDEAS
    12. Perko, Igor, 2017. "Behaviour-based short-term invoice probability of default evaluation," European Journal of Operational Research, Elsevier, vol. 257(3), pages 1045-1054.
    13. repec:taf:quantf:v:16:y:2016:i:12:p:1901-1915 is not listed on IDEAS
    14. repec:eee:jbfina:v:89:y:2018:i:c:p:14-25 is not listed on IDEAS
    15. Sermpinis, Georgios & Stasinakis, Charalampos & Theofilatos, Konstantinos & Karathanasopoulos, Andreas, 2015. "Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms—Support vector regression forecast combinations," European Journal of Operational Research, Elsevier, vol. 247(3), pages 831-846.
    16. repec:eee:ejores:v:268:y:2018:i:1:p:348-360 is not listed on IDEAS
    17. Sermpinis, Georgios & Stasinakis, Charalampos & Rosillo, Rafael & de la Fuente, David, 2017. "European Exchange Trading Funds Trading with Locally Weighted Support Vector Regression," European Journal of Operational Research, Elsevier, vol. 258(1), pages 372-384.

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