Denoised Monte Carlo for option pricing and Greeks estimation

Abstract

We present a novel technique of Monte Carlo error reduction that finds direct application in option pricing and Greeks estimation. The method is applicable to any LSV modelling framework and concerns a broad class of payoffs, including path-dependent and multi-asset cases. Most importantly, it allows to reduce the Monte Carlo error even by an order of magnitude, which is shown in several numerical examples.