In this article, we consider multilevel Monte Carlo for the numerical computation of expectations for stochastic differential equations driven by Lévy processes. The underlying numerical schemes are based on jump-adapted Euler schemes. We prove stable convergence of an idealised scheme. Further, we deduce limit theorems for certain classes of functionals depending on the whole trajectory of the process. In particular, we allow dependence on marginals, integral averages and the supremum of the process. The idealised scheme is related to two practically implementable schemes and corresponding central limit theorems are given. In all cases, we obtain errors of order N −1/2 (log N ) 1/2 in the computational time N which is the same order as obtained in the classical set-up analysed by Giles [Oper.Res. 56 (2008) 607-617]. Finally, we use the central limit theorems to optimise the parameters of the multilevel scheme.

In this article we present an implementation of a multilevel Monte Carlo scheme for Lévy-driven SDEs introduced and analysed in (Dereich and Li, Multilevel Monte Carlo for Lévy-driven SDEs: central limit theorems for adaptive Euler schemes, Ann. Appl. Probab. 26, No. 1, 136-185, 2016 [12]). The scheme is based on direct simulation of Lévy increments. We give an efficient implementation of the algorithm. In particular, we explain direct simulation techniques for Lévy increments. Further, we optimise over the involved parameters and, in particular, the refinement multiplier. This article complements the theoretical considerations of the above reference. We stress that we focus on the case where the frequency of small jumps is particularly high, meaning that the Blumenthal-Getoor index is larger than one.

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