Title: A parallel quasi-Monte Carlo approach to pricing multidimensional American options

Authors: Justin W.L. Wan, Kevin Lai, Adam W. Kolkiewicz, Ken Seng Tan

Addresses: School of Computer Science, University of Waterloo, Canada. ' School of Computer Science, University of Waterloo, Canada. ' Department of Statistics and Actuarial Science, University of Waterloo, Canada. ' Department of Statistics and Actuarial Science, University of Waterloo, Canada; China Institute for Actuarial Science, Central University of Finance and Economics, Beijing, China

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.

Keywords: quasi-Monto Carlo methods; American options; parallel computing; high performance computing; option pricing; computational finance.

DOI: 10.1504/IJHPCN.2006.013487

International Journal of High Performance Computing and Networking, 2006 Vol.4 No.5/6, pp.321 - 330

Available online: 01 May 2007 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article