Abstract
The objective of this paper is to approximate the probability density functions for Ornstein-Uhlenbeck processes that are driven by Levy processes. This is achieved by considering a truncated set of cumulants relating to the Levy measure and expressing these as moments of Gaussian random variables. This can be thought of as a weaker version of Central Limit Theorem. We illustrate the use of these densities via Brownian motion (a benchmark) and Meixner process.
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