摘要翻译：
在这篇文章中，我们提出了一种新的方法来降低离散时间美式期权定价的各种近似方法的计算复杂度。给出了对应于不同空间近似和时间离散程度的连续值估计序列，我们提出了美式期权价格的多级低偏估计。结果表明，所得到的复杂度增益可能相当高，甚至可以达到(\varepsilon^{-1}）的级别，其中(\varepsilon)表示所需的精度。最后以百慕大最大看涨期权的定价为例，说明了多级算法的性能。
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英文标题：
《Pricing American options via multi-level approximation methods》
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作者：
Denis Belomestny, Fabian Dickmann and Tigran Nagapetyan
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最新提交年份：
2013
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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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英文摘要：
  In this article we propose a novel approach to reduce the computational complexity of various approximation methods for pricing discrete time American options. Given a sequence of continuation values estimates corresponding to different levels of spatial approximation and time discretization, we propose a multi-level low biased estimate for the price of an American option. It turns out that the resulting complexity gain can be rather high and can even reach the order (\varepsilon^{-1}) with (\varepsilon) denoting the desired precision. The performance of the proposed multilevel algorithm is illustrated by a numerical example of pricing Bermudan max-call options.
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PDF链接：
https://arxiv.org/pdf/1303.1334