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摘要翻译：
利用改进的协方差矩阵估计作为样本估计的替代被认为是增强投资组合优化的一个重要途径。本文利用1997-2007年间90只高资本美国股票的日收益率，对9种改进的协方差估计方法的性能进行了实证比较。我们发现协方差矩阵估计的有用性很大程度上取决于估计周期T与股票数量N之间的比率、卖空行为的存在与否以及所考虑的绩效指标。当允许卖空时，几种估计方法得到的已实现风险比样本协方差方法得到的风险要小得多。当t/n接近1时尤其如此。此外，许多估计方法降低了负投资组合权重的比例，但在多样化程度上却没有取得什么改善。相反，当不允许卖空且t>n时，所考虑的方法在已实现风险方面不能超过样本协方差，但可以给出比样本协方差更多样化的投资组合。当t<n时，使用样本协方差矩阵和伪逆给出的投资组合性能很差。
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英文标题：
《When do improved covariance matrix estimators enhance portfolio
optimization? An empirical comparative study of nine estimators》
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作者：
Ester Pantaleo, Michele Tumminello, Fabrizio Lillo and Rosario N.
Mantegna
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最新提交年份：
2010
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分类信息：
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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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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一级分类：Quantitative Finance 数量金融学
二级分类：Risk Management 风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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英文摘要：
The use of improved covariance matrix estimators as an alternative to the sample estimator is considered an important approach for enhancing portfolio optimization. Here we empirically compare the performance of 9 improved covariance estimation procedures by using daily returns of 90 highly capitalized US stocks for the period 1997-2007. We find that the usefulness of covariance matrix estimators strongly depends on the ratio between estimation period T and number of stocks N, on the presence or absence of short selling, and on the performance metric considered. When short selling is allowed, several estimation methods achieve a realized risk that is significantly smaller than the one obtained with the sample covariance method. This is particularly true when T/N is close to one. Moreover many estimators reduce the fraction of negative portfolio weights, while little improvement is achieved in the degree of diversification. On the contrary when short selling is not allowed and T>N, the considered methods are unable to outperform the sample covariance in terms of realized risk but can give much more diversified portfolios than the one obtained with the sample covariance. When T<N the use of the sample covariance matrix and of the pseudoinverse gives portfolios with very poor performance.
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PDF链接：
https://arxiv.org/pdf/1004.4272
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