Parallel Randomized Quasi-Monte Carlo Simulation for Asian Basket Option Pricing

Abstract

High-dimensional derivatives pricing, such as Asian basket options, poses great computational challenges in practice. In this paper, parallel Randomized Quasi-Monte Carlo (RQMC) simulation method is investigated to tackle this kind of intractable problems. By using the intrinsic nature of “embarrassingly parallel”, parallel RQMC algorithm for Asian basket option pricing is effectively implemented using MPI. Numerical experiments are performed on supercomputer DeepComp6800 and the parallel performance is then analyzed. Our parallel algorithm has exerted perfect performance and good scalability. Parallel computing has greatly improved the computational efficiency of derivatives pricing.