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An Optimization Approach to Weak Approximation of Lévy-Driven Stochastic Differential Equations

By Kenji Kashima and Reiichiro Kawai


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 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

Publisher: Springer Verlag
Year: 2010
DOI identifier: 10.1007/978-3-540-93918-4
OAI identifier:

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