摘要翻译：
我们分析了股票收益率的互相关对数据采样频率的依赖性，即Epps效应：对于高分辨率数据，互相关显著小于它们在日常数据上观察到的渐近值。前一个描述意味着改变交易频率应该改变现象的特征时间。对于经验数据来说，这是不正确的：Epps曲线不随市场活动而伸缩。后者的结果表明，现象的时间尺度与市场参与者的反应时间（我们称之为人的时间尺度）有关，与市场活动无关。本文通过互相关分解对Epps效应进行了新的描述。在一个随机游动价格变化模型上检验我们的方法后，我们通过拟合真实世界数据的Epps曲线来证明我们的分析结果是正确的。
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英文标题：
《The Epps effect revisited》
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作者：
Bence Toth, Janos Kertesz
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最新提交年份：
2008
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分类信息：

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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一级分类：Physics        物理学
二级分类：Data Analysis, Statistics and Probability        数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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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：
  We analyse the dependence of stock return cross-correlations on the sampling frequency of the data known as the Epps effect: For high resolution data the cross-correlations are significantly smaller than their asymptotic value as observed on daily data. The former description implies that changing trading frequency should alter the characteristic time of the phenomenon. This is not true for the empirical data: The Epps curves do not scale with market activity. The latter result indicates that the time scale of the phenomenon is connected to the reaction time of market participants (this we denote as human time scale), independent of market activity. In this paper we give a new description of the Epps effect through the decomposition of cross-correlations. After testing our method on a model of generated random walk price changes we justify our analytical results by fitting the Epps curves of real world data.
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PDF链接：
https://arxiv.org/pdf/0704.1099