Credit Risk Models with Incomplete Information
by Xin Guo of the University of California, Berkeley,
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