Multilayer networks for measuring interconnectedness among global stock markets through the lens of trading volume-price relationship

Abstract

Using daily data spanning from 5 January 2004 to 22 November 2022, we quantify the spillover effects between 42 global stock markets. Specifically, combining causal structure learning and Elastic-Net-VAR methods, we innovatively construct multilayer causal networks based on volume-price relationship. Then, we analyze the network characteristics of multilayer spillover networks from system and market levels. Our findings indicate that there is heterogeneity in risk spillovers of price and volume networks, highlighting how trading volume spillover network play an important role in the risk contagion. Furthermore, multilayer interconnected networks confirm the risk spillovers between volume and price, and it exhibits significant differences compared to single-layer network. In addition, at system-level, each network layer shows unique network structures and dynamic evolution characteristics. At market-level, global stock markets play different roles in emitting or receiving shocks through various transmission channels. Our study emphasizes the importance of intra- and inter-layer risk propagation in multilayer networks based on volume and price, and has significant implications for developing investment strategies and global portfolio risk management.