Complex network analysis of global stock market co-movement during the COVID-19 pandemic based on intraday open-high-low-close data

This study uses complex network analysis to investigate global stock market comovement during the black swan event of the Coronavirus Disease 2019 (COVID-19) pandemic. We propose a novel method for calculating stock price index correlations based on open-high-low-close (OHLC) data. More intraday information can be utilized compared with the widely used return-based method. Hypothesis testing was used to select the edges incorporated in the network to avoid a rigid setting of the artifcial threshold. The topologies of the global stock market complex network constructed using 70 important global stock price indices before (2017-2019) and after (2020-2022) the COVID-19 outbreak were examined. The evidence shows that the degree centrality of the OHLC data-based global stock price index complex network has better power-law distribution characteristics than a return-based network. The global stock market co-movement characteristics are revealed, and the fnancial centers of the developed, emerging, and frontier markets are identifed. Using centrality indicators, we also illustrate changes in the importance of individual stock price indices during the COVID-19 pandemic. Based on these fndings, we provide suggestions for investors and policy regulators to improve their international portfolios and strengthen their national fnancial risk preparedness.