High Performance Financial Simulation Using Randomized Quasi-Monte Carlo Methods

TL;DR

This paper surveys Monte Carlo and quasi-Monte Carlo methods in GPU-based financial simulations, highlighting recent advances, GPU-specific features, and comparing implementations for pricing financial derivatives.

Contribution

It introduces a recent randomized quasi-Monte Carlo method and compares it with existing GPU implementations for financial pricing models.

Findings

GPU-accelerated Monte Carlo methods significantly improve computational speed.

The new randomized quasi-Monte Carlo method offers competitive accuracy and efficiency.

GPU architecture features influence the design of efficient (quasi) Monte Carlo algorithms.

Abstract

GPU computing has become popular in computational finance and many financial institutions are moving their CPU based applications to the GPU platform. Since most Monte Carlo algorithms are embarrassingly parallel, they benefit greatly from parallel implementations, and consequently Monte Carlo has become a focal point in GPU computing. GPU speed-up examples reported in the literature often involve Monte Carlo algorithms, and there are software tools commercially available that help migrate Monte Carlo financial pricing models to GPU. We present a survey of Monte Carlo and randomized quasi-Monte Carlo methods, and discuss existing (quasi) Monte Carlo sequences in GPU libraries. We discuss specific features of GPU architecture relevant for developing efficient (quasi) Monte Carlo methods. We introduce a recent randomized quasi-Monte Carlo method, and compare it with some of the existing implementations on GPU, when they are used in pricing caplets in the LIBOR market model and mortgage backed securities.