Quasi-Monte Carlo methods for the Heston model

TL;DR

This paper adapts quasi-Monte Carlo methods to the Heston model, demonstrating their advantages over traditional Monte Carlo approaches and proposing tailored bridge constructions for enhanced efficiency.

Contribution

It introduces a novel application of quasi-Monte Carlo methods directly to the Heston model, including tailored bridge techniques and extensions for various financial derivatives.

Findings

Quasi-Monte Carlo methods outperform Monte Carlo in the Heston model.

Tailored bridge constructions significantly improve quasi-Monte Carlo efficiency.

Extensions enable computation of greeks, barrier options, and multi-asset pricing.

Abstract

In this paper, we discuss the application of quasi-Monte Carlo methods to the Heston model. We base our algorithms on the Broadie-Kaya algorithm, an exact simulation scheme for the Heston model. As the joint transition densities are not available in closed-form, the Linear Transformation method due to Imai and Tan, a popular and widely applicable method to improve the effectiveness of quasi-Monte Carlo methods, cannot be employed in the context of path-dependent options when the underlying price process follows the Heston model. Consequently, we tailor quasi-Monte Carlo methods directly to the Heston model. The contributions of the paper are threefold: We firstly show how to apply quasi-Monte Carlo methods in the context of the Heston model and the SVJ model, secondly that quasi-Monte Carlo methods improve on Monte Carlo methods, and thirdly how to improve the effectiveness of quasi-Monte Carlo methods by using bridge constructions tailored to the Heston and SVJ models. Finally, we provide some extensions for computing greeks, barrier options, multidimensional and multi-asset pricing, and the 3/2 model.