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Monte Carlo Methods and Applications

Managing Editor: Sabelfeld, Karl K.

Editorial Board: Binder, Kurt / Bouleau, Nicolas / Chorin, Alexandre J. / Dimov, Ivan / Dubus, Alain / Egorov, Alexander D. / Ermakov, Sergei M. / Halton, John H. / Heinrich, Stefan / Kalos, Malvin H. / Lepingle, D. / Makarov, Roman / Mascagni, Michael / Mathe, Peter / Niederreiter, Harald / Platen, Eckhard / Sawford, Brian R. / Schmid, Wolfgang Ch. / Schoenmakers, John / Simonov, Nikolai A. / Sobol, Ilya M. / Spanier, Jerry / Talay, Denis


CiteScore 2018: 0.66

SCImago Journal Rank (SJR) 2018: 0.319
Source Normalized Impact per Paper (SNIP) 2018: 0.720

Mathematical Citation Quotient (MCQ) 2017: 0.25

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1569-3961
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Volume 24, Issue 4

Issues

On the implementation of multilevel Monte Carlo simulation of the stochastic volatility and interest rate model using multi-GPU clusters

Harold A. Lay / Zane Colgin / Viktor Reshniak / Abdul Q. M. Khaliq
  • Department of Mathematical Sciences and Center for Computational Science, Middle Tennessee State University, 1301 East Main Street, Murfreesboro, TN 37130, USA
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Published Online: 2018-10-30 | DOI: https://doi.org/10.1515/mcma-2018-2025

Abstract

We explore different methods of solving systems of stochastic differential equations by first implementing the Euler–Maruyama and Milstein methods with a Monte Carlo simulation on a CPU. The performance of the methods is significantly improved through the recently developed antithetic multilevel Monte Carlo estimator, which yields a computation complexity of 𝒪(ϵ-2) root-mean-square error and does so without the approximation of Lévy areas. Further improvements in performance are gained by moving the algorithms to a GPU - first on a single device and then on a multi-GPU cluster. Our GPU implementation of the antithetic multilevel Monte Carlo displays a major speedup in computation when compared with many commonly used approaches in the literature. While our work is focused on the simulation of the stochastic volatility and interest rate model, it is easily extendable to other stochastic systems, and it is of particular interest to those with non-diagonal, non-commutative noise.

Keywords: Stochastic volatility; stochastic interest rate; multilevel Monte Carlo; GPU; multi-node GPU cluster

MSC 2010: 60G99

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About the article

Received: 2018-05-02

Revised: 2018-09-27

Accepted: 2018-10-14

Published Online: 2018-10-30

Published in Print: 2018-12-01


Citation Information: Monte Carlo Methods and Applications, Volume 24, Issue 4, Pages 309–321, ISSN (Online) 1569-3961, ISSN (Print) 0929-9629, DOI: https://doi.org/10.1515/mcma-2018-2025.

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