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摘要翻译：
研究了利用蒙特卡罗和非参数回归的百慕大期权定价问题。基于由延拓值的一些估计构造的次优停止规则，我们导出了下有偏估计的最优非渐近界。这些估计可以是不同性质的，它们可以是局部的，也可以是全局的，唯一的要求是这些估计与真连续值的偏差在概率上可以是一致有界的。作为说明，我们讨论了一类局部多项式估计，它在某些正则性条件下得到了具有这种性质的延拓值估计。
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英文标题：
《Pricing Bermudan options using nonparametric regression: optimal rates
of convergence for lower estimates》
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作者：
Denis Belomestny
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最新提交年份：
2009
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分类信息：
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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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：
The problem of pricing Bermudan options using Monte Carlo and a nonparametric regression is considered. We derive optimal non-asymptotic bounds for a lower biased estimate based on the suboptimal stopping rule constructed using some estimates of continuation values. These estimates may be of different nature, they may be local or global, with the only requirement being that the deviations of these estimates from the true continuation values can be uniformly bounded in probability. As an illustration, we discuss a class of local polynomial estimates which, under some regularity conditions, yield continuation values estimates possessing this property.
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PDF链接：
https://arxiv.org/pdf/0907.5599
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