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摘要翻译：
股票之间的互相关矩阵包含了不同策略和时间范围的交易者之间的多重相互作用。本文利用最大重叠离散小波变换计算不同时间尺度上的相关矩阵，进而探讨滑动时间窗上的特征值谱。特征值谱在不同时间和尺度上的动态特性提供了对所涉及的众多成分之间相互作用的洞察力。特征值动力学检验了中频率和高频股票回报，相关的相关结构显示依赖于时间和规模。此外，Epps效应是建立使用这种多元方法和分析在更长的尺度比以前的研究。对特征值时间序列的划分表明，在很短的尺度下，当最大特征值最大时，出现负收益。最后，一个投资组合优化说明了时间尺度信息在风险管理中的重要性。
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英文标题：
《Multiscaled Cross-Correlation Dynamics in Financial Time-Series》
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作者：
Thomas Conlon, Heather J. Ruskin, Martin Crane
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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：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：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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英文摘要：
The cross correlation matrix between equities comprises multiple interactions between traders with varying strategies and time horizons. In this paper, we use the Maximum Overlap Discrete Wavelet Transform to calculate correlation matrices over different timescales and then explore the eigenvalue spectrum over sliding time windows. The dynamics of the eigenvalue spectrum at different times and scales provides insight into the interactions between the numerous constituents involved. Eigenvalue dynamics are examined for both medium and high-frequency equity returns, with the associated correlation structure shown to be dependent on both time and scale. Additionally, the Epps effect is established using this multivariate method and analyzed at longer scales than previously studied. A partition of the eigenvalue time-series demonstrates, at very short scales, the emergence of negative returns when the largest eigenvalue is greatest. Finally, a portfolio optimization shows the importance of timescale information in the context of risk management.
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PDF链接：
https://arxiv.org/pdf/1001.0497
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