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


  • Greg M. Gupton


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. Copyright Banca Monte dei Paschi di Siena SpA, 2005

Suggested Citation

  • 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

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    Cited by:

    1. Jiří Witzany & Michal Rychnovský & Pavel Charamza, 2012. "Survival Analysis in LGD Modeling," European Financial and Accounting Journal, University of Economics, Prague, vol. 2012(1), pages 6-27.
    2. repec:ris:apltrx:0329 is not listed on IDEAS
    3. Curti, Filippo & Migueis, Marco, 2016. "Predicting Operational Loss Exposure Using Past Losses," Finance and Economics Discussion Series 2016-2, Board of Governors of the Federal Reserve System (US), revised 12 Oct 2016.
    4. repec:wsi:ijtafx:v:20:y:2017:i:04:n:s0219024917500236 is not listed on IDEAS
    5. Dannenberg, Henry, 2006. "Die Verlustverteilung des unternehmerischen Forderungsausfallrisikos – Eine simulationsbasierte Modellierung," IWH Discussion Papers 10/2006, Halle Institute for Economic Research (IWH).
    6. repec:eco:journ1:2017-03-99 is not listed on IDEAS
    7. Yashkir, Olga & Yashkir, Yuriy, 2013. "Loss Given Default Modelling: Comparative Analysis," MPRA Paper 46147, University Library of Munich, Germany.
    8. repec:eee:empfin:v:47:y:2018:i:c:p:246-262 is not listed on IDEAS
    9. Jiří Witzany, 2009. "Unexpected Recovery Risk and LGD Discount Rate Determination," European Financial and Accounting Journal, University of Economics, Prague, vol. 2009(1), pages 61-84.
    10. 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.

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