In multivariate time series systems, it has been observed that certain groups of variables partially lead the evolution of the system, while other variables follow this evolution with a time delay; the result is a lead-lag structure amongst the time series variables. In this paper, we propose a method for the detection of lead-lag clusters of time series in multivariate systems. We demonstrate that the web of pairwise lead-lag relationships between time series can be helpfully construed as a directed network, for which there exist suitable algorithms for the detection of pairs of lead-lag clusters with high pairwise imbalance. Within our framework, we consider a number of choices for the pairwise lead-lag metric and directed network clustering components. Our framework is validated on both a synthetic generative model for multivariate lead-lag time series systems and daily real-world US equity prices data. We showcase that our method is able to detect statistically significant lead-lag clusters in the US equity market. We study the nature of these clusters in the context of the empirical finance literature on lead-lag relations and demonstrate how these can be used for the construction of predictive financial signals.
翻译:在多变时间序列系统中,观察到某些类别的变数部分导致系统演进,而其他变数则随着时间推移而变化;结果是在时间序列变量中形成铅渣结构;在本文件中,我们提出了在多变系统中检测铅渣时间序列组的方法;我们表明,时间序列之间双向铅渣关系网可以被有用地解释为一个定向网络,其中存在适当的算法,可以用来检测铅渣组群中存在高度对称不平衡的铅渣组群。在我们的框架内,我们考虑对称铅渣指标和定向网络组群组成部分的若干选择。我们的框架在多变式铅渣时间序列系统合成基因化模型和美国日常实际公平价格数据上得到验证。我们展示,我们的方法能够探测美国股市中具有统计意义的铅渣组群群。我们在铅渣关系经验金融文献中研究这些组群群群的性质,并展示这些组群群如何用于构建预测性金融信号。