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- W2000678557 abstract "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 path-wise 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." @default.
- W2000678557 created "2016-06-24" @default.
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- W2000678557 date "2010-11-01" @default.
- W2000678557 modified "2023-09-25" @default.
- W2000678557 title "Parallel implementation of Quantization methods for the valuation of swing options on GPGPU" @default.
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- W2000678557 doi "https://doi.org/10.1109/whpcf.2010.5671811" @default.
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