摘要翻译：
我们回顾了以前为研究相关系数对数据分辨率的依赖性（Epps效应）而提出的股票收益互相关的分解方法。通过随机游动/布朗运动的玩具模型和观测时间的无记忆更新过程（即泊松点过程），我们证明了在解析可处理的情况下，通过分解相关关系，我们得到了频率依赖的精确结果。我们还证明，在经验数据的情况下，我们的方法产生了合理的相关性依赖于数据分辨率的拟合。我们的结果表明，Epps现象是高分辨率数据滞后相关有限时间衰减的产物，它不随活度的变化而变化。特征时间是由于人类的时间尺度，即对新闻做出反应所需的时间。
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英文标题：
《Modeling the Epps effect of cross correlations in asset prices》
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作者：
Bence Toth, Balint Toth, Janos Kertesz
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最新提交年份：
2007
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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 review the decomposition method of stock return cross-correlations, presented previously for studying the dependence of the correlation coefficient on the resolution of data (Epps effect). Through a toy model of random walk/Brownian motion and memoryless renewal process (i.e. Poisson point process) of observation times we show that in case of analytical treatability, by decomposing the correlations we get the exact result for the frequency dependence. We also demonstrate that our approach produces reasonable fitting of the dependence of correlations on the data resolution in case of empirical data. Our results indicate that the Epps phenomenon is a product of the finite time decay of lagged correlations of high resolution data, which does not scale with activity. The characteristic time is due to a human time scale, the time needed to react to news.
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PDF链接：
https://arxiv.org/pdf/0704.3798