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

Linlin Xu and Giray Ökten

Quantitative Finance, 2015, vol. 15, issue 8, 1425-1436

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.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:taf:quantf:v:15:y:2015:i:8:p:1425-1436

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DOI: 10.1080/14697688.2015.1032549

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