GPU acceleration of the stochastic grid bundling method for early-exercise options |
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Authors: | Álvaro Leitao Cornelis W. Oosterlee |
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Affiliation: | 1. Delft Institute of Applied Mathematics (DIAM), TU Delft, Delft, The Netherlands;2. Centrum Wiskunde &3. Informatica (CWI), Amsterdam, The Netherlandsa.leitaorodriguez@tudelft.nl;5. Informatica (CWI), Amsterdam, The Netherlands |
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Abstract: | 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. |
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Keywords: | computational finance early-exercise derivatives basket Bermudan options high-dimensional pricing stochastic grid bundling method (SGBM) Monte Carlosimulation least-squares regression high performance computing parallel programming GPGPU compute unified device architecture (CUDA) |
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