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Credit Risk Models with Incomplete Information

by Xin Guo of the University of California, Berkeley,
Robert A. Jarrow of Cornell University & Kamakura Corp., and
Yan Zeng of Bloomberg

June 18, 2008

Abstract: Incomplete information is at the heart of information-based credit risk models. In this paper, we rigorously define incomplete information with the notion of "delayed filtrations". We characterize two distinct types of delayed information, continuous and discrete: the first generated by a time change of filtrations and the second by finitely many marked point processes. This notion unifies the noisy information in Duffie and Lando (2001) and the partial information in Collin-Dufresne et al. (2004). under which structural models are translated into reduced-form intensity-based models. We illustrate through a simple example the importance of this notion of delayed information, as well as the potential pitfall for abusing the Laplacian approximation techniques for calculating the intensity process in an information-based model.

Published in: Mathematics of Operations Research, Vol. 34, No. 2, (May 2009), pp. 320-332.

Previously titled: Information Reduction in Credit Risk Models

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