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

Authors:
Justin W. L. Wan;Kevin Lai;Adam W. Kolkiewicz;Ken Seng Tan
Affiliations:
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
Venue:
International Journal of High Performance Computing and Networking
Year:
2006

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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.