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GPU acceleration of the Seven-League Scheme for large time step simulations of stochastic differential equations

Author

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  • Shuaiqiang Liu
  • Graziana Colonna
  • Lech A. Grzelak
  • Cornelis W. Oosterlee

Abstract

Monte Carlo simulation is widely used to numerically solve stochastic differential equations. Although the method is flexible and easy to implement, it may be slow to converge. Moreover, an inaccurate solution will result when using large time steps. The Seven League scheme, a deep learning-based numerical method, has been proposed to address these issues. This paper generalizes the scheme regarding parallel computing, particularly on Graphics Processing Units (GPUs), improving the computational speed.

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  • Shuaiqiang Liu & Graziana Colonna & Lech A. Grzelak & Cornelis W. Oosterlee, 2023. "GPU acceleration of the Seven-League Scheme for large time step simulations of stochastic differential equations," Papers 2302.05170, arXiv.org.
    • Handle: RePEc:arx:papers:2302.05170
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    References listed on IDEAS

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    1. L. A. Grzelak & J. A. S. Witteveen & M. Suárez-Taboada & C. W. Oosterlee, 2019. "The stochastic collocation Monte Carlo sampler: highly efficient sampling from ‘expensive’ distributions," Quantitative Finance, Taylor & Francis Journals, vol. 19(2), pages 339-356, February.
    2. Eckhard Platen, 1999. "An Introduction to Numerical Methods for Stochastic Differential Equations," Research Paper Series 6, Quantitative Finance Research Centre, University of Technology, Sydney.
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