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Parallel implementation of a Quantization algorithm for pricing American style options on GPGPU

G. Pages, B. Wilbertz
Lab. de Probabilites & Modeles Aleatoires, Univ. Pierre & Marie Curie (P6), Paris, France
International Conference on High Performance Computing and Simulation (HPCS), 2010

@conference{wilbertz2010parallel,

   title={Parallel implementation of a Quantization algorithm for pricing American style options on GPGPU},

   author={Wilbertz, B.},

   booktitle={High Performance Computing and Simulation (HPCS), 2010 International Conference on},

   pages={370–375},

   year={2010},

   organization={IEEE}

}

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The Quantization Tree algorithm has proven to be quite an efficient tool for the evaluation of financial derivatives with non-vanilla exercise rights as American-, Bermudan-or Swing options. Nevertheless, it relies heavily on a fast computation of the transition probabilities in the underlying Quantization Tree. Since this estimation is typically done by Monte-Carlo simulations, it is appealing to take advantage of the massive parallel computing capabilities of modern GPGPU-devices. We present in this article a parallel implementation of the transition probability estimation for a Gaussian 2-factor model in CUDA. Since we have to deal in this case with a huge amount of data and quite long MC-paths, it turned out that the naive pathwise parallel implementation is not optimal. We therefore present a time-layer wise parallelization which can better exploit the parallel computing power of GPGPU-devices by using faster memory structures.
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