Antithetic multilevel Monte Carlo estimation for multi-dimensional SDEs without L\'{e}vy area simulation

TL;DR

This paper introduces an antithetic multilevel Monte Carlo estimator for multi-dimensional SDEs that avoids Lévy area simulation, achieving high variance reduction and computational efficiency for option pricing.

Contribution

It develops a novel antithetic estimator that attains high variance reduction without Lévy area simulation, improving MLMC efficiency for multi-dimensional SDEs.

Findings

Achieves $O( ext{Δt}^2)$ variance for smooth payoffs

Attains nearly $O( ext{Δt}^{3/2})$ variance for piecewise smooth payoffs

Reduces complexity to $O( ext{ε}^{-2})$ for European and Asian options

Abstract

In this paper we introduce a new multilevel Monte Carlo (MLMC) estimator for multi-dimensional SDEs driven by Brownian motions. Giles has previously shown that if we combine a numerical approximation with strong order of convergence $O(\Delta t)$ with MLMC we can reduce the computational complexity to estimate expected values of functionals of SDE solutions with a root-mean-square error of $\epsilon$ from $O(\epsilon^{-3})$ to $O(\epsilon^{-2})$. However, in general, to obtain a rate of strong convergence higher than $O(\Delta t^{1/2})$ requires simulation, or approximation, of L\'{e}vy areas. In this paper, through the construction of a suitable antithetic multilevel correction estimator, we are able to avoid the simulation of L\'{e}vy areas and still achieve an $O(\Delta t^2)$ multilevel correction variance for smooth payoffs, and almost an $O(\Delta t^{3/2})$ variance for piecewise smooth payoffs, even though there is only $O(\Delta t^{1/2})$ strong convergence. This results in an $O(\epsilon^{-2})$ complexity for estimating the value of European and Asian put and call options.