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Advancing Loss Given Default Prediction Models: How the Quiet Have Quickened

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  • Greg M. Gupton

Abstract

We describe LossCalc™ version 2.0: the Moody's KMV model to predict loss given default (LGD), the equivalent of (1 − recovery rate). LossCalc is a statistical model that applies multiple predictive factors at different information levels: collateral, instrument, firm, industry, country and the macroeconomy to predict LGD. We find that distance‐to‐default measures (from the Moody's KMV structural model of default likelihood) compiled at both the industry and firm levels are predictive of LGD. We find that recovery rates worldwide are predictable within a common statistical framework, which suggests that the estimation of economic firm value (which is then available to allocate to claimants according to each country's bankruptcy laws) is a dominant step in LGD determination. LossCalc is built on a global dataset of 3,026 recovery observations for loans, bonds and preferred stock from 1981 to 2004. This dataset includes 1,424 defaults of both public and private firms – both rated and unrated instruments – in all industries. We demonstrate out‐of‐sample and out‐of‐time LGD model validation. The model significantly improves on the use of historical recovery averages to predict LGD.

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  • 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.
    • Handle: RePEc:bla:ecnote:v:34:y:2005:i:2:p:185-230
    DOI: 10.1111/j.0391-5026.2005.00149.x
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    References listed on IDEAS

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    1. Dirk Tasche, 2004. "The single risk factor approach to capital charges in case of correlated loss given default rates," Papers cond-mat/0402390, arXiv.org, revised Feb 2004.
    2. Ivailo Izvorski, 1997. "Recovery Ratios and Survival Times for Corporate Bonds," IMF Working Papers 1997/084, International Monetary Fund.
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    1. Mustapha Ammari & Ghizlane Lakhnati, 2017. "Loss Given Default Estimating by the Conditional Minimum Value," International Journal of Economics and Financial Issues, Econjournals, vol. 7(3), pages 779-785.
    2. Maria Stefanova, 2012. "Recovery Risiko in der Kreditportfoliomodellierung," Springer Books, Springer, number 978-3-8349-4226-5, December.
    3. Jiří Witzany & Michal Rychnovský & Pavel Charamza, 2012. "Survival Analysis in LGD Modeling," European Financial and Accounting Journal, Prague University of Economics and Business, vol. 2012(1), pages 6-27.
    4. Stefan Hlawatsch, 2009. "A Framework for LGD Validation of Retail Portfolios," FEMM Working Papers 09025, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
    5. Dannenberg, Henry, 2006. "Die Verlustverteilung des unternehmerischen Forderungsausfallrisikos – Eine simulationsbasierte Modellierung," IWH Discussion Papers 10/2006, Halle Institute for Economic Research (IWH).
    6. Filippo Curti & Marco Migueis, 2016. "Predicting Operational Loss Exposure Using Past Losses," Finance and Economics Discussion Series 2016-2, Board of Governors of the Federal Reserve System (U.S.).
    7. Rumyantseva, Ekaterina & Furmanov, Kirill, 2017. "Realisation of mortgage property: Survival analysis," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 48, pages 22-43.
    8. Yashkir, Olga & Yashkir, Yuriy, 2013. "Loss Given Default Modelling: Comparative Analysis," MPRA Paper 46147, University Library of Munich, Germany.
    9. Jiří Witzany, 2009. "Unexpected Recovery Risk and LGD Discount Rate Determination," European Financial and Accounting Journal, Prague University of Economics and Business, vol. 2009(1), pages 61-84.
    10. 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.
    11. 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.

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