High-performance financial simulation using randomized quasi-Monte Carlo methods

Abstract

Graphics Processing Unit (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.

Keywords:

• GPU
• Monte Carlo
• Randomized quasi-Monte Carlo
• LIBOR
• Mortgage-backed securities

JEL Classification:

• C63

Disclosure statement

No potential conflict of interest was reported by the authors.