摘要翻译：
本文研究了任意高维样本均值-方差组合的一致性，这些组合是基于输入参数的贝叶斯估计或收缩估计以及加权抽样的。在一个渐近条件下，当资产数量在规模上与样本大小相当时，我们通过提供投资组合的样本外性能的确定性等价物来描述估计风险。以前的估计代表了一种量化方法，即改进的投资组合结构超出标准结构的风险低估和收益高估的数量。后者是众所周知的，如果不加以纠正，这些偏差会导致不准确和过于乐观的夏普投资决策。我们的结果是基于最近在随机矩阵理论领域的贡献。随着渐近分析，该分析框架允许我们发现偏差修正改善了典型投资组合结构的样本外表现。一些数值模拟验证了我们的理论结果。
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英文标题：
《Performance analysis and optimal selection of large mean-variance
  portfolios under estimation risk》
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作者：
Francisco Rubio, Xavier Mestre, Daniel P. Palomar
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最新提交年份：
2011
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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：Computer Science        计算机科学
二级分类：Information Theory        信息论
分类描述：Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
涵盖信息论和编码的理论和实验方面。包括ACM学科类E.4中的材料，并与H.1.1有交集。
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一级分类：Mathematics        数学
二级分类：Information Theory        信息论
分类描述：math.IT is an alias for cs.IT. Covers theoretical and experimental aspects of information theory and coding.
它是cs.it的别名。涵盖信息论和编码的理论和实验方面。
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英文摘要：
  We study the consistency of sample mean-variance portfolios of arbitrarily high dimension that are based on Bayesian or shrinkage estimation of the input parameters as well as weighted sampling. In an asymptotic setting where the number of assets remains comparable in magnitude to the sample size, we provide a characterization of the estimation risk by providing deterministic equivalents of the portfolio out-of-sample performance in terms of the underlying investment scenario. The previous estimates represent a means of quantifying the amount of risk underestimation and return overestimation of improved portfolio constructions beyond standard ones. Well-known for the latter, if not corrected, these deviations lead to inaccurate and overly optimistic Sharpe-based investment decisions. Our results are based on recent contributions in the field of random matrix theory. Along with the asymptotic analysis, the analytical framework allows us to find bias corrections improving on the achieved out-of-sample performance of typical portfolio constructions. Some numerical simulations validate our theoretical findings.
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PDF链接：
https://arxiv.org/pdf/1110.3460