Common Failings: How Corporate Defaults are Correlated

36 Pages Posted: 2 Jan 2006

See all articles by Sanjiv Ranjan Das

Sanjiv Ranjan Das

Santa Clara University - Leavey School of Business

Darrell Duffie

Stanford University - Graduate School of Business; National Bureau of Economic Research (NBER); Canadian Derivatives Institute

Nikunj Kapadia

University of Massachusetts Amherst - Department of Finance

Leandro Saita

Independent

Multiple version iconThere are 2 versions of this paper

Abstract

We develop, and apply to data on U.S. corporations from 1979-2004, tests of the standard doubly-stochastic assumption under which firms' default times are correlated only as implied by the correlation of factors determining their default intensities. This assumption is violated in the presence of contagion or "frailty" (unobservable explanatory variables that are correlated across firms). Our tests do not depend on the time-series properties of default intensities. The data do not support the joint hypothesis of well specified default intensities and the doubly-stochastic assumption. There is also some evidence of default clustering in excess of that implied by the doubly-stochastic model with the given intensities.

Keywords: Correlated default, doubly stochastic, contagion, frailty

JEL Classification: G1, G3, G33

Suggested Citation

Das, Sanjiv Ranjan and Duffie, James Darrell and Kapadia, Nikunj and Saita, Leandro, Common Failings: How Corporate Defaults are Correlated. Journal of Finance, Forthcoming, Available at SSRN: https://ssrn.com/abstract=873149

Sanjiv Ranjan Das

Santa Clara University - Leavey School of Business ( email )

Department of Finance
316M Lucas Hall
Santa Clara, CA 95053
United States

HOME PAGE: http://srdas.github.io/

James Darrell Duffie

Stanford University - Graduate School of Business ( email )

655 Knight Way
Knight Management Center
Stanford, CA 94305-7298
United States
650-723-1976 (Phone)
650-725-8916 (Fax)

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Canadian Derivatives Institute ( email )

3000, chemin de la Côte-Sainte-Catherine
Montréal, Québec H3T 2A7
Canada

Nikunj Kapadia (Contact Author)

University of Massachusetts Amherst - Department of Finance ( email )

Amherst, MA 01003
United States
413-545-5643 (Phone)
413-545-5600 (Fax)

HOME PAGE: http://www-unix.oit.umass.edu/~nkapadia/

Leandro Saita

Independent

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
468
Abstract Views
2,421
Rank
93,936
PlumX Metrics