A parallel quasi-Monte Carlo approach to pricing multidimensional American options. (2nd May 2007)

• Record Type: Journal Article
• Authors: Wan, Justin W.L.; Lai, Kevin; Kolkiewicz, Adam W.; Tan, Ken Seng
• Is Part Of: International journal of high performance computing and networking. Volume 4:Number 5/6(2006)
• Page Start: 321
• Page End: 330
• Publication Date: 2007-05-02
• Subjects: quasi-Monto Carlo methods -- American options -- parallel computing -- high performance computing -- option pricing -- computational finance; High performance computing -- Periodicals; Computer networks -- Periodicals; High performance computing; Periodicals; 004.05
• Languages: English
• ISSNs: 1740-0562

Abstract

In this paper, we develop parallel algorithms for pricing American options on multiple assets. Our parallel methods are based on the Low Discrepancy (LD) mesh method which combines the quasi-Monte Carlo technique with the stochastic mesh method. We present two approaches to parallelise the backward recursion step, which is the most computational intensive part of the LD mesh method. We perform parallel run time analysis of the proposed methods and prove that both parallel approaches are scalable. The algorithms are implemented using MPI. The parallel efficiency of the methods are demonstrated by pricing several American options, and near optimal speedup results are presented.