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LossCalc: Moody's Model for Predicting Loss Given Default (LGD)

by Greg M. Gupton of Moody's|KMV, and
Roger M. Stein of Moody's|KMV

February 2002

Abstract: This report describes and documents LossCalc, Moody's model for predicting loss given default (LGD): the equivalent of (1-recovery rate). LGD is of natural interest to investors and lenders wishing to estimate future credit losses. LossCalc is a robust and validated model of United States LGD for bonds, loans and preferred stock. It produces estimates of LGD for defaults occurring immediately and for defaults occurring in one year. These two point-in-time estimates can be used to predict LGD over differing holding periods.

LossCalc is a statistical model that incorporates information on instrument, firm, industry and economy to predict LGD. It improves upon traditional reliance on historical recovery averages. The model is based on over 1,800 observations of U.S. recovery values of defaulted loans, bonds and preferred stock covering the last two decades. This dataset includes over 900 defaulted public and private firms in all industries.

We believe LossCalc is a meaningful addition to the practice of credit risk management and a step forward in answering the call for rigor that the BIS has outlined in their recently proposed Basel Capital Accord.

JEL Classification: C52, G20, G33.

Books Referenced in this paper:  (what is this?)

Download paper (1,189K PDF) 32 pages

Readers of this may be interested in: LossCalc v2: Dynamic Prediction of LGD
Advancing Loss Given Default Prediction Models: How the quiet have quickened

Most Cited Books within Recoveries/LGD Papers

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