Regression-Based Variance Reduction Approach for Strong Approximation Schemes
In this paper we present a novel approach towards variance reduction for discretised diffusion processes. The proposed approach involves specially constructed control variates and allows for a significant reduction in the variance for the terminal functionals. In this way the complexity order of the standard Monte Carlo algorithm (ε−3) can be reduced down to ε−2 log(ε−1) in case of the Euler scheme with ε being the precision to be achieved. These theoretical results are illustrated by several numerical examples.