We 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. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Kashima, K., & Kawai, R. (2010). An optimization approach to weak approximation of lévy-driven stochastic differential equations. Lecture Notes in Control and Information Sciences, 398, 263–272. https://doi.org/10.1007/978-3-540-93918-4_24
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