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摘要翻译：
将最近提出的一种基于最小二乘优化的重要性抽样策略应用于Libor市场模型的蒙特卡罗模拟。这种最小二乘重要性抽样(LSIS)允许通过简单实现的快速预模拟算法自动优化试验类内的抽样分布。通过几个数值例子，我们表明LSIS可以非常有效地降低蒙特卡罗估计量的方差，尤其是当与分层抽样相结合时，计算速度通常会提高数量级。
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英文标题：
《Least Squares Importance Sampling for Libor Market Models》
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作者：
Luca Capriotti
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最新提交年份：
2007
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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：Physics 物理学
二级分类：Other Condensed Matter 其他凝聚态物质
分类描述：Work in condensed matter that does not fit into the other cond-mat classifications
在不适合其他cond-mat分类的凝聚态物质中工作
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一级分类：Physics 物理学
二级分类：Physics and Society 物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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英文摘要：
A recently introduced Importance Sampling strategy based on a least squares optimization is applied to the Monte Carlo simulation of Libor Market Models. Such Least Squares Importance Sampling (LSIS) allows the automatic optimization of the sampling distribution within a trial class by means of a quick presimulation algorithm of straightforward implementation. With several numerical examples we show that LSIS can be extremely effective in reducing the variance of Monte Carlo estimators often resulting, especially when combined with stratified sampling, in computational speed-ups of orders of magnitude.
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PDF链接：
https://arxiv.org/pdf/0711.0223
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