IDEAS home Printed from https://ideas.repec.org/a/eee/ecmode/v81y2019icp231-241.html
   My bibliography  Save this article

Modeling recovery rate for leveraged loans

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0264999318318339
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.econmod.2019.04.006?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xia, Yufei & Zhao, Junhao & He, Lingyun & Li, Yinguo & Yang, Xiaoli, 2021. "Forecasting loss given default for peer-to-peer loans via heterogeneous stacking ensemble approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1590-1613.
    2. Kaposty, Florian & Kriebel, Johannes & Löderbusch, Matthias, 2020. "Predicting loss given default in leasing: A closer look at models and variable selection," International Journal of Forecasting, Elsevier, vol. 36(2), pages 248-266.
    3. Li, Aimin & Li, Zhiyong & Bellotti, Anthony, 2023. "Predicting loss given default of unsecured consumer loans with time-varying survival scores," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    4. Marc Gürtler & Marvin Zöllner, 2023. "Heterogeneities among credit risk parameter distributions: the modality defines the best estimation method," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 251-287, March.
    5. 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.
    6. Nazemi, Abdolreza & Fatemi Pour, Farnoosh & Heidenreich, Konstantin & Fabozzi, Frank J., 2017. "Fuzzy decision fusion approach for loss-given-default modeling," European Journal of Operational Research, Elsevier, vol. 262(2), pages 780-791.
    7. Nazemi, Abdolreza & Rezazadeh, Hani & Fabozzi, Frank J. & Höchstötter, Markus, 2022. "Deep learning for modeling the collection rate for third-party buyers," International Journal of Forecasting, Elsevier, vol. 38(1), pages 240-252.
    8. Betz, Jennifer & Kellner, Ralf & Rösch, Daniel, 2018. "Systematic Effects among Loss Given Defaults and their Implications on Downturn Estimation," European Journal of Operational Research, Elsevier, vol. 271(3), pages 1113-1144.
    9. Jennifer Betz & Ralf Kellner & Daniel Rösch, 2021. "Time matters: How default resolution times impact final loss rates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 619-644, June.
    10. Ruey-Ching Hwang & Chih-Kang Chu & Kaizhi Yu, 2021. "Predicting the Loss Given Default Distribution with the Zero-Inflated Censored Beta-Mixture Regression that Allows Probability Masses and Bimodality," Journal of Financial Services Research, Springer;Western Finance Association, vol. 59(3), pages 143-172, June.
    11. Miller, Patrick & Töws, Eugen, 2018. "Loss given default adjusted workout processes for leases," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 189-201.
    12. Yuta Tanoue & Satoshi Yamashita & Hideaki Nagahata, 2020. "Comparison study of two-step LGD estimation model with probability machines," Risk Management, Palgrave Macmillan, vol. 22(3), pages 155-177, September.
    13. Christophe Hurlin & Jérémy Leymarie & Antoine Patin, 2018. "Loss functions for LGD model comparison," Working Papers halshs-01516147, HAL.
    14. Salvatore D. Tomarchio & Antonio Punzo, 2019. "Modelling the loss given default distribution via a family of zero‐and‐one inflated mixture models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1247-1266, October.
    15. Bastos, João A. & Matos, Sara M., 2022. "Explainable models of credit losses," European Journal of Operational Research, Elsevier, vol. 301(1), pages 386-394.
    16. Starosta, Wojciech, 2021. "Loss given default decomposition using mixture distributions of in-default events," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1187-1199.
    17. Nazemi, Abdolreza & Baumann, Friedrich & Fabozzi, Frank J., 2022. "Intertemporal defaulted bond recoveries prediction via machine learning," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1162-1177.
    18. Wang, Hong & Forbes, Catherine S. & Fenech, Jean-Pierre & Vaz, John, 2020. "The determinants of bank loan recovery rates in good times and bad – New evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 177(C), pages 875-897.
    19. Thamayanthi Chellathurai, 2017. "Probability Density Of Recovery Rate Given Default Of A Firm’S Debt And Its Constituent Tranches," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(04), pages 1-34, June.
    20. Hwang, Ruey-Ching & Chu, Chih-Kang & Yu, Kaizhi, 2020. "Predicting LGD distributions with mixed continuous and discrete ordinal outcomes," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1003-1022.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecmode:v:81:y:2019:i:c:p:231-241. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30411 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.