2015
GPU acceleration of the stochastic grid bundling method for early-exercise options
Publication
Publication
Journal of Computational Mathematics , Volume 92 - Issue 12 p. 2433- 2454
In this work, a parallel graphics processing units (GPU) version of the Monte Carlo stochastic grid
bundling method (SGBM) for pricing multi-dimensional early-exercise options is presented. To extend
the method’s applicability, the problem dimensions and the number of bundles will be increased drastically. This makes SGBM very expensive in terms of computational costs on conventional hardware systems based on central processing units. A parallelization strategy of the method is developed and the general purpose computing on graphics processing units paradigm is used to reduce the execution time. An improved technique for bundling asset paths, which is more efficient on parallel hardware is introduced. Thanks to the performance of the GPU version of SGBM, a general approach for computing the early-exercise policy is proposed. Comparisons between sequential and GPU parallel versions are presented
Additional Metadata | |
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Science Press | |
doi.org/10.1080/00207160.2015.1067689 | |
Journal of Computational Mathematics | |
Organisation | Scientific Computing |
Leitao Rodriguez, Á., & Oosterlee, K. (2015). GPU acceleration of the stochastic grid bundling method for early-exercise options. Journal of Computational Mathematics, 92(12), 2433–2454. doi:10.1080/00207160.2015.1067689 |