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2010

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

10 years 1 months ago
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
Rüdiger Frey, Wolfgang Runggaldier
Added 25 Jan 2011
Updated 25 Jan 2011
Type Journal
Year 2010
Where FS
Authors Rüdiger Frey, Wolfgang Runggaldier
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