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Downturn LGD modeling using quantile regression

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  • Krüger, Steffen
  • Rösch, Daniel

Abstract

Literature on Losses Given Default (LGD) usually focuses on mean predictions, even though losses are extremely skewed and bimodal. This paper proposes a Quantile Regression (QR) approach to get a comprehensive view on the entire probability distribution of losses. The method allows new insights on covariate effects over the whole LGD spectrum. In particular, middle quantiles are explainable by observable covariates while tail events, e.g., extremely high LGDs, seem to be rather driven by unobservable random events. A comparison of the QR approach with several alternatives from recent literature reveals advantages when evaluating downturn and unexpected credit losses. In addition, we identify limitations of classical mean prediction comparisons and propose alternative goodness of fit measures for the validation of forecasts for the entire LGD distribution.

Suggested Citation

  • Krüger, Steffen & Rösch, Daniel, 2017. "Downturn LGD modeling using quantile regression," Journal of Banking & Finance, Elsevier, vol. 79(C), pages 42-56.
    • Handle: RePEc:eee:jbfina:v:79:y:2017:i:c:p:42-56
    DOI: 10.1016/j.jbankfin.2017.03.001
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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. 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.
    4. Calabrese, Raffaella & Zenga, Michele, 2010. "Bank loan recovery rates: Measuring and nonparametric density estimation," Journal of Banking & Finance, Elsevier, vol. 34(5), pages 903-911, May.
    5. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731.
    6. 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.
    7. Sudheer Chava & Catalina Stefanescu & Stuart Turnbull, 2011. "Modeling the Loss Distribution," Management Science, INFORMS, vol. 57(7), pages 1267-1287, July.
    8. 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.
    9. Koenker, Roger & Bassett, Gilbert, Jr, 1982. "Robust Tests for Heteroscedasticity Based on Regression Quantiles," Econometrica, Econometric Society, vol. 50(1), pages 43-61, January.
    10. Altman, Edward I. & Kalotay, Egon A., 2014. "Ultimate recovery mixtures," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 116-129.
    11. 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.
    12. 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.
    13. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    14. Calabrese, Raffaella, 2014. "Downturn Loss Given Default: Mixture distribution estimation," European Journal of Operational Research, Elsevier, vol. 237(1), pages 271-277.
    15. 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.
    16. Somers, Mark & Whittaker, Joe, 2007. "Quantile regression for modelling distributions of profit and loss," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1477-1487, December.
    17. Yashkir, Olga & Yashkir, Yuriy, 2013. "Loss Given Default Modelling: Comparative Analysis," MPRA Paper 46147, University Library of Munich, Germany.
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    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. Frank Ranganai Matenda & Mabutho Sibanda & Eriyoti Chikodza & Victor Gumbo, 2022. "Corporate Loan Recovery Rates under Downturn Conditions in a Developing Economy: Evidence from Zimbabwe," Risks, MDPI, vol. 10(10), pages 1-24, October.
    3. Shi, Baofeng & Chi, Guotai & Li, Weiping, 2020. "Exploring the mismatch between credit ratings and loss-given-default: A credit risk approach," Economic Modelling, Elsevier, vol. 85(C), pages 420-428.

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    More about this item

    Keywords

    Loss given default; Downturn; Quantile regression; Recovery; Validation;
    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

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