Truncated control variates for weak approximation schemes

TL;DR

This paper introduces a truncated control variate method to improve the efficiency of regression-based variance reduction techniques in weak approximation schemes, significantly reducing computation time without increasing complexity.

Contribution

It proposes a novel truncation approach for control variates that enhances existing variance reduction methods in stochastic simulations.

Findings

Significant reduction in computing time demonstrated

Complexity remains unchanged with the new truncation method

Numerical example confirms improved performance

Abstract

In this paper we present an enhancement of the regression-based variance reduction approaches recently proposed in Belomestny et al. This enhancement is based on a truncation of the control variate and allows for a significant reduction of the computing time, while the complexity stays of the same order. The performances of the proposed truncated algorithms are illustrated by a numerical example.