摘要翻译：
通过研究相关网络和相关矩阵的谱性质，我们研究了美国市场上交易的美国股票的成对行业指数之间的相关性动态。本研究采用K.French和E.Fama计算的49个行业指数时间序列，时间序列从1969年7月到2011年12月，时间跨度超过40年。研究表明，各行业指数之间的相关性既有快速的动态，也有缓慢的动态。这种缓慢动态的时间尺度超过五年，表明在不同的时期可以进行不同程度的投资多样化。除了这种缓慢的动力学之外，我们还发现了一种与外源或内源性事件相关的快速动力学。我们使用的快速时间尺度是一个月的时间尺度，评估时间周期是一个3个月的时间周期。通过每月研究相关动力学，我们能够检测到相关矩阵的第一和第二特征值快速变化的两个例子。第一次发生在网络泡沫时期（1999年3月至2001年4月），第二次发生在次贷危机影响最大的时期（2008年8月至2009年8月）。
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英文标题：
《Evolution of correlation structure of industrial indices of US equity
markets》
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作者：
Giuseppe Buccheri, Stefano Marmi and Rosario N. Mantegna
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最新提交年份：
2013
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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：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 investigate the dynamics of correlations present between pairs of industry indices of US stocks traded in US markets by studying correlation based networks and spectral properties of the correlation matrix. The study is performed by using 49 industry index time series computed by K. French and E. Fama during the time period from July 1969 to December 2011 that is spanning more than 40 years. We show that the correlation between industry indices presents both a fast and a slow dynamics. The slow dynamics has a time scale longer than five years showing that a different degree of diversification of the investment is possible in different periods of time. On top to this slow dynamics, we also detect a fast dynamics associated with exogenous or endogenous events. The fast time scale we use is a monthly time scale and the evaluation time period is a 3 month time period. By investigating the correlation dynamics monthly, we are able to detect two examples of fast variations in the first and second eigenvalue of the correlation matrix. The first occurs during the dot-com bubble (from March 1999 to April 2001) and the second occurs during the period of highest impact of the subprime crisis (from August 2008 to August 2009).
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