金融时间序列的相关滤波
Correlation filtering in financial time series
摘要：
我们应用一种方法，通过提取相关交互的网络来从相关系数矩阵中过滤相关信息。该方法成功地生成了具有与最小生成树相同的层次结构的网络，但包含更多的链接，从而产生更丰富的网络拓扑，允许环路和集团。在Tumminello等人的\cite{TumminielloPNAS05}中，我们已经证明了这种方法，应用于美国股票市场的100只股票的金融投资组合，在过滤关于系统的聚类及其在整个系统和每个聚类内的层次结构的相关信息方面是非常有效的。特别地，我们发现三角环和四元集团与市场结构和性质有着重要的关系。在这里，我们应用这种过滤程序的相关性分析在两个不同类型的利率时间序列（16欧元和34美元利率）。
英文摘要：
We apply a method to filter relevant information from the correlation coefficient matrix by extracting a network of relevant interactions. This method succeeds to generate networks with the same hierarchical structure of the Minimum Spanning Tree but containing a larger amount of links resulting in a richer network topology allowing loops and cliques. In Tumminello et al. \cite{TumminielloPNAS05}, we have shown that this method, applied to a financial portfolio of 100 stocks in the USA equity markets, is pretty efficient in filtering relevant information about the clustering of the system and its hierarchical structure both on the whole system and within each cluster. In particular, we have found that triangular loops and 4 element cliques have important and significant relations with the market structure and properties. Here we apply this filtering procedure to the analysis of correlation in two different kind of interest rate time series (16 Eurodollars and 34 US interest rates).
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