RFS Advance Access originally published online on May 19, 2008
Review of Financial Studies 2008 21(3):1339-1369; doi:10.1093/rfs/hhn044
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Forecasting Default with the Merton Distance to Default Model
Ross School of Business, University of Michigan
Ross School of Business, University of Michigan
Address correspondence to either Sreedhar T. Bharath, Ross School of Business, University of Michigan, 701 Tappan Street, Ann Arbor, MI 48109; telephone: (734) 763-0485; e-mail: sbharath{at}umich.edu or Tyler Shumway, Ross School of Business, University of Michigan, 701 Tappan Street, Ann Arbor, MI 48109-1234; telephone: (734) 763-4129; e-mail: shumway{at}umich.edu
JEL Classification: G12, G13, G33
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Abstract |
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We examine the accuracy and contribution of the Merton distance to default (DD) model, which is based on Merton's (1974) bond pricing model. We compare the model to a "naïve" alternative, which uses the functional form suggested by the Merton model but does not solve the model for an implied probability of default. We find that the naïve predictor performs slightly better in hazard models and in out-of-sample forecasts than both the Merton DD model and a reduced-form model that uses the same inputs. Several other forecasting variables are also important predictors, and fitted values from an expanded hazard model outperform Merton DD default probabilities out of sample. Implied default probabilities from credit default swaps and corporate bond yield spreads are only weakly correlated with Merton DD probabilities after adjusting for agency ratings and bond characteristics. We conclude that while the Merton DD model does not produce a sufficient statistic for the probability of default, its functional form is useful for forecasting defaults.
Previously circulated with the title "Forecasting Default with the KMV-Merton Model." This research was supported by the NTT fellowship of the Mitsui Life Center at the Ross School of Business, University of Michigan. We thank seminar participants at Michigan, Boston College, Indian School of Business, Moody's 2006 Credit Risk Conference, and Stanford. We also thank Bill Beaver, Jeff Bohn, Darrell Duffie, Eric Falkenstein, Wayne Ferson, Ravi Jagannathan, Kyle Lundstedt, Ken Singleton, and Jorge Sobehart for their comments. All errors are ours.