This is the authors' final draft of the paper published as Lecture Notes in Control and Information Sciences, 2010, 398, pp. 263-272. The original publication is available at www.springerlink.com. Doi: 10.1007/978-3-540-93918-4We propose an optimization approach to weak approximation of Lévy-driven stochastic differential equations. We employ a mathematical programming framework to obtain numerically upper and lower bound estimates of the target expectation, where the optimization procedure ends up with a polynomial programming problem. An advantage of our approach is that all we need is a closed form of the Lévy measure, not the exact simulation knowledge of the increments or of a shot noise representation for the time discretization approximation. We also investigate methods for approximation at some different intermediate time points simultaneously
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