Estimating option prices using multilevel particle filters

TL;DR

This paper introduces a multilevel particle filter method for option pricing, combining MLMC and particle filters to reduce computational effort and variance in estimating option prices, demonstrated through numerical simulations.

Contribution

The paper proposes a novel multilevel particle filter approach for option valuation, integrating MLMC and particle filtering techniques to improve efficiency.

Findings

MLPF reduces computational effort compared to PF.

MLPF achieves variance reduction in option price estimates.

Numerical simulations confirm efficiency gains for vanilla and exotic options.

Abstract

Option valuation problems are often solved using standard Monte Carlo (MC) methods. These techniques can often be enhanced using several strategies especially when one discretizes the dynamics of the underlying asset, of which we assume follows a diffusion process. We consider the combination of two methodologies in this direction. The first is the well-known multilevel Monte Carlo (MLMC) method, which is known to reduce the computational effort to achieve a given level of mean square error relative to MC in some cases. Sequential Monte Carlo (or the particle filter (PF)) methods have also been shown to be beneficial in many option pricing problems potentially reducing variances by large magnitudes (relative to MC). We propose a multilevel particle filter (MLPF) as an alternative approach to price options. The computational savings obtained in using MLPF over PF for pricing both vanilla and exotic options is demonstrated via numerical simulations.