Credit risk modeling under incomplete information
Abstract
In this work we study a class of credit default models with imperfect information. We combine the ideas of both structural and reduced form models, within a partial observation framework in which the information could even be delayed. Assuming that default is triggered by the touch-down of the firm total asset process to a prescribed and possibly random barrier, our main purpose is to obtain the default probability, as a continuous function of a hidden Markovian factor process, conditioning on the observed continuous and jump information. We show that a "separation principle" of nonlinear filtering is still valid in such a setting, and the default intensity can be estimated through the filtered factor process, which is the solution of a Riccati-type of Stochastic SDE driven by the underlying Brownian motion and counting process. Some Bayesian inference theory is also applied to obtain our solutions.
Degree
Ph.D.
Advisors
Ma, Purdue University.
Subject Area
Mathematics
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