An efficient graphics processing unit-based parallel algorithm for pricing multi-asset American options

Authors:
Duy Minh Dang;Christina C. Christara;Kenneth R. Jackson
Affiliations:
Department of Computer Science, University of Toronto, Toronto, ONM5S 3G4, Canada;Department of Computer Science, University of Toronto, Toronto, ONM5S 3G4, Canada;Department of Computer Science, University of Toronto, Toronto, ONM5S 3G4, Canada
Venue:
Concurrency and Computation: Practice & Experience
Year:
2012

Citing 4
Cited 1

Quadratic Convergence for Valuing American Options Using a Penalty Method

SIAM Journal on Scientific Computing
ADI schemes for higher-order nonlinear diffusion equations

Applied Numerical Mathematics
Pricing Multi-Asset American Options: A Finite Element Method-of-Lines with Smooth Penalty

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Unconditional stability of second-order ADI schemes applied to multi-dimensional diffusion equations with mixed derivative terms

Applied Numerical Mathematics

Special Issue for the Workshop on High Performance Computational Finance

Concurrency and Computation: Practice & Experience

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Abstract

We develop highly efficient parallel PDE-based pricing methods on graphics processing units (GPUs) for multi-asset American options. Our pricing approach is built upon a combination of a discrete penalty approach for the linear complementarity problem arising because of the free boundary and a GPU-based parallel alternating direction implicit approximate factorization technique with finite differences on uniform grids for the solution of the linear algebraic system arising from each penalty iteration. A timestep size selector implemented efficiently on GPUs is used to further increase the efficiency of the methods. We demonstrate the efficiency and accuracy of the parallel numerical methods by pricing American options written on three assets. Copyright © 2011 John Wiley & Sons, Ltd.