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Modeling recovery rate for leveraged loans

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  • Chen, Xiaowei
  • Wang, Gang
  • Zhang, Xiangting

Abstract

Leveraged loan has become an import risk contributor to the wholesale portfolio of a financial institution and an accurate evaluation of the recovery rate of leveraged loans is crucial for risk-based decision making by banks. To achieve this, we utilize a simple two-stage model framework conditional on loan and its borrower's characteristics. Under this framework, three kinds of models and two combining mechanisms are studied by using a subset of leveraged loan data filtered from Moody's Ultimate Recovery Data (URD). The in-sample and out-of-sample results show that three-split model with parallel combining mechanism yields more accurate predictions of ultimate recovery rates for leveraged loans. It is shown that the percentage of debt that is junior relative to the issuance in the issuer's capital structure is the most important determinant of the leveraged loan recovery outcomes. Recovery rates for Leveraged loans and for non-leveraged-loan debts are also compared. Empirical studies show that they have different influential factors.

Suggested Citation

  • Chen, Xiaowei & Wang, Gang & Zhang, Xiangting, 2019. "Modeling recovery rate for leveraged loans," Economic Modelling, Elsevier, vol. 81(C), pages 231-241.
    • Handle: RePEc:eee:ecmode:v:81:y:2019:i:c:p:231-241
    DOI: 10.1016/j.econmod.2019.04.006
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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. 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.
    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. João Bastos, 2014. "Ensemble Predictions of Recovery Rates," Journal of Financial Services Research, Springer;Western Finance Association, vol. 46(2), pages 177-193, October.
    6. Yao, Xiao & Crook, Jonathan & Andreeva, Galina, 2017. "Enhancing two-stage modelling methodology for loss given default with support vector machines," European Journal of Operational Research, Elsevier, vol. 263(2), pages 679-689.
    7. 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.
    8. Miller, Patrick & Töws, Eugen, 2018. "Loss given default adjusted workout processes for leases," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 189-201.
    9. Hurlin, Christophe & Leymarie, Jérémy & Patin, Antoine, 2018. "Loss functions for Loss Given Default model comparison," European Journal of Operational Research, Elsevier, vol. 268(1), pages 348-360.
    10. Tanoue, Yuta & Kawada, Akihiro & Yamashita, Satoshi, 2017. "Forecasting loss given default of bank loans with multi-stage model," International Journal of Forecasting, Elsevier, vol. 33(2), pages 513-522.
    11. Altman, Edward I. & Kalotay, Egon A., 2014. "Ultimate recovery mixtures," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 116-129.
    12. Leow, Mindy & Mues, Christophe, 2012. "Predicting loss given default (LGD) for residential mortgage loans: A two-stage model and empirical evidence for UK bank data," International Journal of Forecasting, Elsevier, vol. 28(1), pages 183-195.
    13. 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.
    14. 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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    Cited by:

    1. Kundu, Shohini, 2023. "The externalities of fire sales: evidence from collateralized loan obligations," ESRB Working Paper Series 141, European Systemic Risk Board.

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    More about this item

    Keywords

    Credit risk; Ultimate recovery rate; Loss given default; Two-stage model; Leveraged loan;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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