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摘要翻译：
稳健可靠的协方差估计在金融和许多其他应用中起着决定性的作用。一类重要的估计量是基于因子模型的。在这里，我们通过大量的蒙特卡罗模拟表明，由统计因子分析模型导出的协方差矩阵表现出一种系统误差，这种系统误差类似于众所周知的样本协方差矩阵谱的系统误差。此外，我们还引入了方向方差调整(DVA)算法，减小了系统误差。在对美国、欧洲和香港市场的一个全面的实证研究中，我们表明我们提出的方法导致了投资组合配置的改进。
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英文标题：
《Directional Variance Adjustment: improving covariance estimates for
high-dimensional portfolio optimization》
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作者：
Daniel Bartz, Kerr Hatrick, Christian W. Hesse, Klaus-Robert M\"uller,
Steven Lemm
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最新提交年份：
2012
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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：Computational Engineering, Finance, and Science 计算工程、金融和科学
分类描述：Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
涵盖了计算机科学在科学、工程和金融领域复杂系统的数学建模中的应用。这里的论文是跨学科和面向应用的，集中在技术和工具，使挑战性的计算模拟能够执行，其中往往需要使用超级计算机或分布式计算平台。包括ACM学科课程J.2、J.3和J.4（经济学）中的材料。
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一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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英文摘要：
Robust and reliable covariance estimates play a decisive role in financial and many other applications. An important class of estimators is based on Factor models. Here, we show by extensive Monte Carlo simulations that covariance matrices derived from the statistical Factor Analysis model exhibit a systematic error, which is similar to the well-known systematic error of the spectrum of the sample covariance matrix. Moreover, we introduce the Directional Variance Adjustment (DVA) algorithm, which diminishes the systematic error. In a thorough empirical study for the US, European, and Hong Kong market we show that our proposed method leads to improved portfolio allocation.
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PDF链接：
https://arxiv.org/pdf/1109.3069
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