Lead-lag detection and network clustering for multivariate time series with an application to the US equity market

• URL: http://arxiv.org/abs/2201.08283v1
• Date: Thu, 20 Jan 2022 16:39:57 GMT
• Title: Lead-lag detection and network clustering for multivariate time series with an application to the US equity market
• Authors: Stefanos Bennett, Mihai Cucuringu, Gesine Reinert
• Abstract summary: We show that a web of pairwise lead-lag relationships between time series can be helpfully construed as a directed network. Within our framework, we consider a number of choices for the pairwise lead-lag metric and directed network clustering components. We showcase that our method is able to detect statistically significant lead-lag clusters in the US equity market.
• Score: 4.963115946610031
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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