摘要翻译：
Markowitz著名的均值-方差投资组合优化理论假定基础资产收益的均值和协方差是已知的。在实践中，它们是未知的，必须从历史数据中估计。将估计值插入假定已知参数的有效前沿，导致投资组合可能表现不佳，并具有反直觉的资产配置权重；这被称为“Markowitz优化之谜”。在回顾了文献中解决这些困难的不同方法后，我们解释了这个谜的根本原因，并提出了一种新的解决方法。新方法不仅比以前的方法提供了实质性的改进，而且它还允许灵活的建模，包括动态特征和历史数据训练样本的基本分析，如仿真和实证研究所示。
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英文标题：
《Mean--variance portfolio optimization when means and covariances are
  unknown》
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作者：
Tze Leung Lai, Haipeng Xing, Zehao Chen
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最新提交年份：
2011
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分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Quantitative Finance        数量金融学
二级分类：Portfolio Management        项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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英文摘要：
  Markowitz's celebrated mean--variance portfolio optimization theory assumes that the means and covariances of the underlying asset returns are known. In practice, they are unknown and have to be estimated from historical data. Plugging the estimates into the efficient frontier that assumes known parameters has led to portfolios that may perform poorly and have counter-intuitive asset allocation weights; this has been referred to as the "Markowitz optimization enigma." After reviewing different approaches in the literature to address these difficulties, we explain the root cause of the enigma and propose a new approach to resolve it. Not only is the new approach shown to provide substantial improvements over previous methods, but it also allows flexible modeling to incorporate dynamic features and fundamental analysis of the training sample of historical data, as illustrated in simulation and empirical studies.
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PDF链接：
https://arxiv.org/pdf/1108.0996