Investigating the Performance of Non-Gaussian Stochastic Intensity Models in the Calibration of Credit Default Swap Spreads

Most important financial models assume randomness is explained through a normal random variable because, in general, use of alternative models is obstructed by the difficulty of calibrating and simulating them. Here we empirically study credit default swap pricing models under a reduced-form framework assuming different dynamics for the default intensity process. We explore pricing performance and parameter stability during the highly volatile period from June 30, 2008 to December 31, 2010 for different classes of processes driven by Brownian motion, three non-Gaussian Lévy processes, and a Sato process. The models are analyzed from both a static and dynamic perspective.

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