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| Credit Risk Models with Incomplete Information by Xin Guo of the University of California at Berkeley, January 21, 2007 Abstract: We consider the problem of introducing incomplete information into credit risk models. We formulate two distinct notions of delayed information, the first generated by a time change of filtrations and the second generated by finitely many marked point processes. These two notions unify the noisy information in Duffie and Lando (2001) and the partial information in Collin-Dufresne et al. (2004). Under this framework, we show how delayed information, such as those available to market participants, transform structural models 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. Previously titled: Information Reduction in Credit Risk Models Books Referenced in this Paper: (what is this?)
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