Pricing credit derivatives under incomplete information: a nonlinear-filtering approach

This paper considers a general reduced form pricing model for credit derivatives where default intensities are driven by some factor process X. The process X is not directly observable for investors in secondary markets; rather, their information set consists of the default history and of noisy price observation for traded credit products. In this context the pricing of credit derivatives leads to a challenging nonlinear filtering problem. We provide recursive updating rules for the filter, derive a finite dimensional filter for the case where X follows a finite state Markov chain and propose a novel particle filtering algorithm. A numerical case study illustrates the properties of the proposed algorithms. Keywords Credit derivatives, nonlinear filtering, marked point processes AMS classification : 91B28, 93E11, 60G55 JEL classification: G13, C11

• Type: Journal
• Year: 2010
• Where: FS
• Authors: Rüdiger Frey, Wolfgang Runggaldier
• Keywords: Credit Derivatives, FS 2010, General Reduced Form, Nonlinear filtering Problem