摘要翻译：
美式和多重行权期权的定价是数学金融学中一个极具挑战性的问题。对于马尔可夫框架下的最优停止或随机控制问题，通常采用最小二乘蒙特卡罗方法（Longstaff-Schwartz方法）来估计倒向动态规划原理中产生的条件期望。不幸的是，这些最小二乘蒙特卡罗方法相当缓慢，并且由于逆向动态规划原理中的依赖结构，不允许并行实现；是否在蒙特卡罗层次上，在时间层层次上，这个问题。因此，我们在本文中提出了一种计算条件期望的量化方法，它允许在蒙特卡罗水平上直接并行化。此外，我们还可以为AR(1)进程在时域中进一步并行化，它利用更快的内存结构，从而最大限度地提高并行执行。最后，我们给出了该方法的CUDA实现的数值结果。与串行CPU实现相比，这样的实现会带来令人印象深刻的加速。
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英文标题：
《GPGPUs in computational finance: Massive parallel computing for American
  style options》
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作者：
Gilles Pag\`es (PMA), Benedikt Wilbertz (PMA)
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最新提交年份：
2011
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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一级分类：Mathematics        数学
二级分类：Probability        概率
分类描述：Theory and applications of probability and stochastic processes: e.g. central limit theorems, large deviations, stochastic differential equations, models from statistical mechanics, queuing theory
概率论与随机过程的理论与应用：例如中心极限定理，大偏差，随机微分方程，统计力学模型，排队论
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一级分类：Quantitative Finance        数量金融学
二级分类：Pricing of Securities        证券定价
分类描述：Valuation and hedging of financial securities, their derivatives, and structured products
金融证券及其衍生产品和结构化产品的估值和套期保值
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英文摘要：
  The pricing of American style and multiple exercise options is a very challenging problem in mathematical finance. One usually employs a Least-Square Monte Carlo approach (Longstaff-Schwartz method) for the evaluation of conditional expectations which arise in the Backward Dynamic Programming principle for such optimal stopping or stochastic control problems in a Markovian framework. Unfortunately, these Least-Square Monte Carlo approaches are rather slow and allow, due to the dependency structure in the Backward Dynamic Programming principle, no parallel implementation; whether on the Monte Carlo levelnor on the time layer level of this problem. We therefore present in this paper a quantization method for the computation of the conditional expectations, that allows a straightforward parallelization on the Monte Carlo level. Moreover, we are able to develop for AR(1)-processes a further parallelization in the time domain, which makes use of faster memory structures and therefore maximizes parallel execution. Finally, we present numerical results for a CUDA implementation of this methods. It will turn out that such an implementation leads to an impressive speed-up compared to a serial CPU implementation.
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PDF链接：
https://arxiv.org/pdf/1101.3228