摘要翻译：
本文给出了一个并行算法，当按比例交易费用应用于标的资产交易时，计算美式期权的买进价格和买进价格。该算法计算重组二叉树的价格，是为现代多核处理器设计的。虽然平行期权定价已经得到了很好的研究，但现有的定价方法都没有考虑交易成本。我们提出的算法将二项式树划分成块。在任何一轮计算中，块被进一步划分为分配给不同处理器的区域。为了最大限度地减少负载不平衡，在每个新轮开始之前动态地调整节点对处理器的分配。在一轮内和连续两轮之间都需要同步。算法的并行加速比与所用处理器的数量成正比。该并行算法在C/C++环境下通过POSIX线程实现，并在一台8处理器的机器上进行了测试。在美式看跌期权定价中，采用8个处理器，1500个时间步，并行加速比为5.26，并行效率为65.75%。
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英文标题：
《Parallel Binomial American Option Pricing with (and without) Transaction
  Costs》
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作者：
Nan Zhang and Alet Roux and Tomasz Zastawniak
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Distributed, Parallel, and Cluster Computing        分布式、并行和集群计算
分类描述：Covers fault-tolerance, distributed algorithms, stabilility, parallel computation, and cluster computing. Roughly includes material in ACM Subject Classes C.1.2, C.1.4, C.2.4, D.1.3, D.4.5, D.4.7, E.1.
包括容错、分布式算法、稳定性、并行计算和集群计算。大致包括ACM学科类C.1.2、C.1.4、C.2.4、D.1.3、D.4.5、D.4.7、E.1中的材料。
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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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英文摘要：
  We present a parallel algorithm that computes the ask and bid prices of an American option when proportional transaction costs apply to the trading of the underlying asset. The algorithm computes the prices on recombining binomial trees, and is designed for modern multi-core processors. Although parallel option pricing has been well studied, none of the existing approaches takes transaction costs into consideration. The algorithm that we propose partitions a binomial tree into blocks. In any round of computation a block is further partitioned into regions which are assigned to distinct processors. To minimise load imbalance the assignment of nodes to processors is dynamically adjusted before each new round starts. Synchronisation is required both within a round and between two successive rounds. The parallel speedup of the algorithm is proportional to the number of processors used. The parallel algorithm was implemented in C/C++ via POSIX Threads, and was tested on a machine with 8 processors. In the pricing of an American put option, the parallel speedup against an efficient sequential implementation was 5.26 using 8 processors and 1500 time steps, achieving a parallel efficiency of 65.75%.
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PDF链接：
https://arxiv.org/pdf/1110.2477