GPU acceleration of the stochastic grid bundling method for early-exercise options

Alvaro Leitao Rodriguez, Kees Oosterlee

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2433-2454
Number of pages22
JournalInternational Journal of Computer Mathematics
Issue number12
Publication statusPublished - 2 Sep 2015


  • basket Bermudan options
  • computational finance
  • compute unified device architecture (CUDA)
  • early-exercise derivatives
  • high performance computing
  • high-dimensional pricing
  • least-squares regression
  • Monte Carlosimulation
  • parallel programming
  • stochastic grid bundling method (SGBM)

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