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摘要翻译：
通过检验滑动时间窗上的特征值谱，研究了多元金融时间序列的等时互相关矩阵的动力学性质。标准普尔500和道琼斯欧洲斯托克50指数的实证结果表明，在这些时间窗内，互相关矩阵的小特征值的动态与最大特征值的动态相反。这种行为与时间窗口的大小和所审查的股票数量无关。然后提出了一个基本的单因素模型，该模型捕捉了经验数据特征值谱的主要动力学特征。通过在单因素模型中增加扰动（导致“市场加部门”模型），增加了额外的部门特征，导致与经验数据相比的反向参与比率。通过对特征值时间序列的划分，我们进而表明负指数收益（drawows）与最大特征值最大的周期相关，而正指数收益（drawups）与最大特征值最小的周期相关。相关动力学的研究为不同策略的交易者的集体行为提供了一些见解。
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英文标题：
《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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英文摘要：
The dynamics of the equal-time cross-correlation matrix of multivariate financial time series is explored by examination of the eigenvalue spectrum over sliding time windows. Empirical results for the S&P 500 and the Dow Jones Euro Stoxx 50 indices reveal that the dynamics of the small eigenvalues of the cross-correlation matrix, over these time windows, oppose those of the largest eigenvalue. This behaviour is shown to be independent of the size of the time window and the number of stocks examined. A basic one-factor model is then proposed, which captures the main dynamical features of the eigenvalue spectrum of the empirical data. Through the addition of perturbations to the one-factor model, (leading to a 'market plus sectors' model), additional sectoral features are added, resulting in an Inverse Participation Ratio comparable to that found for empirical data. By partitioning the eigenvalue time series, we then show that negative index returns, (drawdowns), are associated with periods where the largest eigenvalue is greatest, while positive index returns, (drawups), are associated with periods where the largest eigenvalue is smallest. The study of correlation dynamics provides some insight on the collective behaviour of traders with varying strategies.
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PDF链接：
https://arxiv.org/pdf/1002.0321
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