标题： 金融时间序列中的多尺度交叉相关动力学 显示英文标题

标题： Multiscaled Cross-Correlation Dynamics in Financial Time-Series

摘要： 交叉相关矩阵涉及不同策略和时间范围的交易者之间的多种互动。 在本文中，我们使用最大重叠离散小波变换在不同时间尺度上计算相关矩阵，然后研究滑动时间窗口上的特征值谱。 不同时间和尺度下的特征值谱动态为涉及的众多组成成分之间的互动提供了见解。 对中等和高频股票收益的特征值动态进行了研究，相关的相关结构被证明依赖于时间和尺度。 此外，使用这种多变量方法建立了埃普斯效应，并在比以前研究更长的尺度上进行了分析。 特征值时间序列的划分显示，在非常短的尺度下，当最大特征值最大时，负收益出现。 最后，投资组合优化展示了时间尺度信息在风险管理背景中的重要性。 显示英文摘要

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.