IDEAS home Printed from https://ideas.repec.org/a/eee/empfin/v47y2018icp246-262.html
   My bibliography  Save this article

A copula sample selection model for predicting multi-year LGDs and Lifetime Expected Losses

Author

Listed:
  • Krüger, Steffen
  • Oehme, Toni
  • Rösch, Daniel
  • Scheule, Harald

Abstract

Recent credit risk literature has proposed (i) sample selection models for dependencies between the one-year Probability of Default (PD) and Loss Given Default (LGD), and (ii) multi-year approaches which are limited to default risk. This paper provides a model for the simultaneous prediction of continuous default times and multi-year LGDs. These measures are paramount to predict term structures of LGDs and Lifetime Expected Losses for the revised loan loss provisioning framework of IFRS 9 and US GAAP (current expected credit loss, CECL). The model includes a variation of copulas and corrects for sample selection bias of LGDs, which are only observed given a default event. We find empirical evidence that bonds which default closer to origination tend to generate higher LGDs. The model enables more precise estimates of Lifetime Expected Losses and prevents a severe underestimation in contrast to more restricted credit risk models.

Suggested Citation

  • Krüger, Steffen & Oehme, Toni & Rösch, Daniel & Scheule, Harald, 2018. "A copula sample selection model for predicting multi-year LGDs and Lifetime Expected Losses," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 246-262.
    • Handle: RePEc:eee:empfin:v:47:y:2018:i:c:p:246-262
    DOI: 10.1016/j.jempfin.2018.04.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S092753981830029X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jempfin.2018.04.001?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. Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007. "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Elsevier, vol. 83(3), pages 635-665, March.
    2. Darrell Duffie & Andreas Eckner & Guillaume Horel & Leandro Saita, 2009. "Frailty Correlated Default," Journal of Finance, American Finance Association, vol. 64(5), pages 2089-2123, October.
    3. John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008. "In Search of Distress Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2899-2939, December.
    4. Benjamin Bade & Daniel Rösch & Harald Scheule, 2011. "Default and Recovery Risk Dependencies in a Simple Credit Risk Model," European Financial Management, European Financial Management Association, vol. 17(1), pages 120-144, January.
    5. Murray D. Smith, 2003. "Modelling sample selection using Archimedean copulas," Econometrics Journal, Royal Economic Society, vol. 6(1), pages 99-123, June.
    6. Silvia Ferrari & Francisco Cribari-Neto, 2004. "Beta Regression for Modelling Rates and Proportions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(7), pages 799-815.
    7. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    8. repec:uts:ppaper:v:17:y:2011:i:1:p:120-144 is not listed on IDEAS
    9. 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.
    10. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    11. Jun Pan & Kenneth J. Singleton, 2008. "Default and Recovery Implicit in the Term Structure of Sovereign CDS Spreads," Journal of Finance, American Finance Association, vol. 63(5), pages 2345-2384, October.
    12. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    13. 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.
    14. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    15. T Bellotti & J Crook, 2009. "Credit scoring with macroeconomic variables using survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1699-1707, December.
    16. Jankowitsch, Rainer & Nagler, Florian & Subrahmanyam, Marti G., 2014. "The determinants of recovery rates in the US corporate bond market," Journal of Financial Economics, Elsevier, vol. 114(1), pages 155-177.
    17. Sanjiv R. Das & Darrell Duffie & Nikunj Kapadia & Leandro Saita, 2007. "Common Failings: How Corporate Defaults Are Correlated," Journal of Finance, American Finance Association, vol. 62(1), pages 93-117, February.
    18. Kiefer, Nicholas M., 2009. "Default estimation for low-default portfolios," Journal of Empirical Finance, Elsevier, vol. 16(1), pages 164-173, January.
    19. Greg M. Gupton, 2005. "Advancing Loss Given Default Prediction Models: How the Quiet Have Quickened," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 34(2), pages 185-230, July.
    20. Sudheer Chava & Catalina Stefanescu & Stuart Turnbull, 2011. "Modeling the Loss Distribution," Management Science, INFORMS, vol. 57(7), pages 1267-1287, July.
    21. D Rösch & H Scheule, 2014. "Forecasting probabilities of default and loss rates given default in the presence of selection," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 393-407, March.
    22. Lando, David & Nielsen, Mads Stenbo, 2010. "Correlation in corporate defaults: Contagion or conditional independence?," Journal of Financial Intermediation, Elsevier, vol. 19(3), pages 355-372, July.
    23. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    24. Marliese Uhrig-Homburg, 2002. "Valuation Of Defaultable Claims – A Survey," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 54(1), pages 24-57, January.
    25. Edward I. Altman & Brooks Brady & Andrea Resti & Andrea Sironi, 2005. "The Link between Default and Recovery Rates: Theory, Empirical Evidence, and Implications," The Journal of Business, University of Chicago Press, vol. 78(6), pages 2203-2228, November.
    26. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    27. Sudheer Chava & Robert A. Jarrow, 2008. "Bankruptcy Prediction with Industry Effects," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 21, pages 517-549, World Scientific Publishing Co. Pte. Ltd..
    28. repec:bla:jfinan:v:53:y:1998:i:4:p:1363-1387 is not listed on IDEAS
    29. Orth, Walter, 2013. "Multi-period credit default prediction with time-varying covariates," Journal of Empirical Finance, Elsevier, vol. 21(C), pages 214-222.
    30. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    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. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
    2. 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).
    3. Morne Joubert & Tanja Verster & Helgard Raubenheimer & Willem D. Schutte, 2021. "Adapting the Default Weighted Survival Analysis Modelling Approach to Model IFRS 9 LGD," Risks, MDPI, vol. 9(6), pages 1-17, June.
    4. Jennifer Betz & Maximilian Nagl & Daniel Rösch, 2022. "Credit line exposure at default modelling using Bayesian mixed effect quantile regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2035-2072, October.
    5. Wattanawongwan, Suttisak & Mues, Christophe & Okhrati, Ramin & Choudhry, Taufiq & So, Mee Chi, 2023. "Modelling credit card exposure at default using vine copula quantile regression," European Journal of Operational Research, Elsevier, vol. 311(1), pages 387-399.
    6. Florian Kaposty & Philipp Klein & Matthias Löderbusch & Andreas Pfingsten, 2022. "Loss given default in SME leasing," Review of Managerial Science, Springer, vol. 16(5), pages 1561-1597, July.
    7. Thi Mai Luong, 2020. "Selection Effects of Lender and Borrower Choices on Risk Measurement, Management and Prudential Regulation," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 3-2020, January-A.

    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. Nguyen, Ha, 2023. "An empirical application of Particle Markov Chain Monte Carlo to frailty correlated default models," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 103-121.
    2. Giesecke, Kay & Longstaff, Francis A. & Schaefer, Stephen & Strebulaev, Ilya, 2011. "Corporate bond default risk: A 150-year perspective," Journal of Financial Economics, Elsevier, vol. 102(2), pages 233-250.
    3. Sigrist, Fabio & Leuenberger, Nicola, 2023. "Machine learning for corporate default risk: Multi-period prediction, frailty correlation, loan portfolios, and tail probabilities," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1390-1406.
    4. Anand Deo & Sandeep Juneja, 2021. "Credit Risk: Simple Closed-Form Approximate Maximum Likelihood Estimator," Operations Research, INFORMS, vol. 69(2), pages 361-379, March.
    5. Ye, Xiaoxia & Yu, Fan & Zhao, Ran, 2022. "Credit derivatives and corporate default prediction," Journal of Banking & Finance, Elsevier, vol. 138(C).
    6. Anand Deo & Sandeep Juneja, 2019. "Credit Risk: Simple Closed Form Approximate Maximum Likelihood Estimator," Papers 1912.12611, arXiv.org.
    7. Chen, Peimin & Wu, Chunchi, 2014. "Default prediction with dynamic sectoral and macroeconomic frailties," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 211-226.
    8. Ruey-Ching Hwang & Huimin Chung & Jiun-Yi Ku, 2013. "Predicting Recurrent Financial Distresses with Autocorrelation Structure: An Empirical Analysis from an Emerging Market," Journal of Financial Services Research, Springer;Western Finance Association, vol. 43(3), pages 321-341, June.
    9. Zhang, Xuan & Ouyang, Ruolan & Liu, Ding & Xu, Liao, 2020. "Determinants of corporate default risk in China: The role of financial constraints," Economic Modelling, Elsevier, vol. 92(C), pages 87-98.
    10. Hwang, Ruey-Ching, 2012. "A varying-coefficient default model," International Journal of Forecasting, Elsevier, vol. 28(3), pages 675-688.
    11. Ruey-Ching Hwang & Jhao-Siang Siao & Huimin Chung & C. Chu, 2011. "Assessing bankruptcy prediction models via information content of technical inefficiency," Journal of Productivity Analysis, Springer, vol. 36(3), pages 263-273, December.
    12. Jens Hilscher & Mungo Wilson, 2017. "Credit Ratings and Credit Risk: Is One Measure Enough?," Management Science, INFORMS, vol. 63(10), pages 3414-3437, October.
    13. Zhang, Xuan & Zhao, Yang & Yao, Xiao, 2022. "Forecasting corporate default risk in China," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1054-1070.
    14. Charitou, Andreas & Dionysiou, Dionysia & Lambertides, Neophytos & Trigeorgis, Lenos, 2013. "Alternative bankruptcy prediction models using option-pricing theory," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2329-2341.
    15. Jobst, Rainer & Kellner, Ralf & Rösch, Daniel, 2020. "Bayesian loss given default estimation for European sovereign bonds," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1073-1091.
    16. Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007. "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Elsevier, vol. 83(3), pages 635-665, March.
    17. Haipeng Xing & Yang Yu, 2018. "Firm’s Credit Risk in the Presence of Market Structural Breaks," Risks, MDPI, vol. 6(4), pages 1-16, December.
    18. Ruey-Ching Hwang & Chih-Kang Chu, 2013. "Forecasting forward defaults: a simple hazard model with competing risks," Quantitative Finance, Taylor & Francis Journals, vol. 14(8), pages 1467-1477, August.
    19. Deniz Anginer & Çelim Yıldızhan, 2018. "Is There a Distress Risk Anomaly? Pricing of Systematic Default Risk in the Cross-section of Equity Returns [The risk-adjusted cost of financial distress]," Review of Finance, European Finance Association, vol. 22(2), pages 633-660.
    20. Chan, Steven & Han, Gaofeng & Zhang, Wenlang, 2016. "How strong are the linkages between real estate and other sectors in China?," Research in International Business and Finance, Elsevier, vol. 36(C), pages 52-72.

    More about this item

    Keywords

    Continuous time-to-default; IFRS 9 and CECL; Lifetime Expected Loss; Loss Given Default; Multi-period; Term structure;
    All these keywords.

    JEL classification:

    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    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:empfin:v:47:y:2018:i:c:p:246-262. 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/jempfin .

    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.