论文标题
发现相对于集体市场运动的相关结构的动态
Uncovering the Dynamics of Correlation Structures Relative to the Collective Market Motion
论文作者
论文摘要
随后时期的财务时间序列的测量相关性随着时间的变化而发生了很大的变化。在研究整个相关矩阵时，通过应用聚类方法可以看到被称为市场状态的准平台模式（称为市场状态）。它们出现，消失或重新出现，但它们由所有股票的集体运动所占据主导地位。在术语中，人们谈到了市场运动，它始终与相关矩阵的最大特征值有关。因此，出现了这个问题，是否可以通过以适当的方式减去主导市场运动来提取有关系统的更多精致信息。为此，我们通过聚类减少级别的相关矩阵引入了一种新方法，该方法是通过从标准相关矩阵中减去属于最大特征值的二元矩阵而获得的。我们在2002年至2016年的近15年内分析了标准普尔500指数的262家公司的每日数据。由此产生的动态截然不同，相应的市场国家在很长一段时间内都是准平台。我们的方法增加了将内源性与外源作用分开的尝试。
The measured correlations of financial time series in subsequent epochs change considerably as a function of time. When studying the whole correlation matrices, quasi-stationary patterns, referred to as market states, are seen by applying clustering methods. They emerge, disappear or reemerge, but they are dominated by the collective motion of all stocks. In the jargon, one speaks of the market motion, it is always associated with the largest eigenvalue of the correlation matrices. Thus the question arises, if one can extract more refined information on the system by subtracting the dominating market motion in a proper way. To this end we introduce a new approach by clustering reduced-rank correlation matrices which are obtained by subtracting the dyadic matrix belonging to the largest eigenvalue from the standard correlation matrices. We analyze daily data of 262 companies of the S&P 500 index over a period of almost 15 years from 2002 to 2016. The resulting dynamics is remarkably different, and the corresponding market states are quasi-stationary over a long period of time. Our approach adds to the attempts to separate endogenous from exogenous effects.
