论文标题
多元时间序列的铅滞后检测和网络聚类，并在美国股票市场应用
Lead-lag detection and network clustering for multivariate time series with an application to the US equity market
论文作者
论文摘要
在多变量时间序列系统中，已经观察到某些变量群会部分领导系统的演变，而其他变量则以时间延迟遵循此演变。结果是时间序列变量之间的铅滞后结构。在本文中，我们提出了一种检测多元系统中时间序列的铅滞后簇的方法。我们证明，时间序列之间的成对铅滞后关系的网络可以有助于解释为有向网络，为此，存在合适的算法，用于检测成对的铅滞后簇成对具有高成对不平衡。在我们的框架内，我们考虑了成对铅滞后指标和定向网络聚类组件的许多选择。我们的框架在用于多元铅滞后时间序列系统和每日现实世界的美国股票价格数据的合成生成模型上得到了验证。我们展示了我们的方法能够检测美国股票市场上具有统计学意义的铅滞后簇。我们在有关铅滞后关系的经验金融文献的背景下研究了这些集群的性质，并证明了这些群集如何用于构建预测金融信号。
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.
