This article is about the strong convergence of the Multilevel Monte-Carlo (MLMC) algorithm when applying with split-step backward Euler (SSBE) scheme to nonlinear jump-diffusion stochastic differential equations (SDEs). The importance of this research is that the underlying process does not enjoy from globally Lipschitz condition and we consider the drift term as one-sided Lipschitz and the payoff function as only locally Lipschitz. We also confirm these theoretical results by numerical experiment for the jump-diffusion process.
