Forecasting Default in the Face of Uncertainty

15 Pages Posted: 17 Mar 2003

See all articles by Lisa R. Goldberg

Lisa R. Goldberg

University of California, Berkeley; Aperio Group

Kay Giesecke

Stanford University - Department of Management Science & Engineering

Date Written: May 4, 2004

Abstract

We give an empirical assessment of I^2, a structural credit model based on incomplete information. In this model, investors cannot observe a firm's default barrier. As a consequence, I^2 exhibits both the economic appeal of a structural model and the tractable pricing formulae and empirical plausibility of a reduced form model. We compare default probability and credit spread forecasts generated by I^2 and the well-known structural models of Merton (1974) and Black & Cox (1976). We find that I^2 reacts more quickly to new information and, unlike the other two models, it forecasts positive short term credit spreads.

Keywords: credit risk, incomplete information, pricing trend, short spreads, default barrier

JEL Classification: G12, G13

Suggested Citation

Goldberg, Lisa R. and Giesecke, Kay, Forecasting Default in the Face of Uncertainty (May 4, 2004). Journal of Derivatives, Vol. 12, No. 1, pp. 14-25, 2004, Available at SSRN: https://ssrn.com/abstract=374080 or http://dx.doi.org/10.2139/ssrn.374080

Lisa R. Goldberg

University of California, Berkeley ( email )

Department of Statistics
367 Evans Hall
Berkeley, CA 94720-3860
United States

Aperio Group ( email )

3 Harbor Drive
Suite 315
Sausalito, CA 94965
United States

Kay Giesecke (Contact Author)

Stanford University - Department of Management Science & Engineering ( email )

475 Via Ortega
Stanford, CA 94305
United States
(650) 723 9265 (Phone)
(650) 723 1614 (Fax)

HOME PAGE: http://https://giesecke.people.stanford.edu

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