Abstract
Most of the important models in finance rest on the assumption that randomness is explained through a normal random variable because, in general, the use of alternative models is obstructed by the difficulty of calibrating and simulating them. In this paper, we empirically study models for pricing credit default swaps under a reduced-form framework, assuming different dynamics for the default intensity process. After reviewing the most recent results on this subject, we explore both pricing performance and parameter stability during the highly volatile period from 30 June 2008 to 31 December 2010 for different classes of processes: one driven by the Brownian motion, three driven by non-Gaussian Levy processes, and the last one driven by a Sato process. The models are analysed from both a static and dynamic perspective.
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