General multilevel Monte Carlo methods for pricing discretely monitored Asian options

TL;DR

This paper introduces advanced multilevel Monte Carlo methods for efficiently pricing discretely monitored Asian options across various stochastic models, significantly reducing computational time compared to traditional methods.

Contribution

The paper develops a general multilevel Monte Carlo framework that provides unbiased estimators with improved efficiency for pricing Asian options under multiple complex stochastic models.

Findings

Achieves unbiased estimators with standard deviation O(ε) in O(m + (1/ε)^2) time.

Outperforms conventional Monte Carlo by a factor of order m.

Validates approach through numerical experiments across diverse models.

Abstract

We describe general multilevel Monte Carlo methods that estimate the price of an Asian option monitored at $m$ fixed dates. Our approach yields unbiased estimators with standard deviation $O(\epsilon)$ in $O(m + (1/\epsilon)^{2})$ expected time for a variety of processes including the Black-Scholes model, Merton's jump-diffusion model, the Square-Root diffusion model, Kou's double exponential jump-diffusion model, the variance gamma and NIG exponential Levy processes and, via the Milstein scheme, processes driven by scalar stochastic differential equations. Using the Euler scheme, our approach estimates the Asian option price with root mean square error $O(\epsilon)$ in $O(m+(\ln(\epsilon)/\epsilon)^{2})$ expected time for processes driven by multidimensional stochastic differential equations. Numerical experiments confirm that our approach outperforms the conventional Monte Carlo method by a factor of order $m$.