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

Wenyang Huang (), Huiwen Wang (), Yigang Wei () and Julien Chevallier ()
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Wenyang Huang: China Agricultural University
Huiwen Wang: Beihang University
Yigang Wei: Beihang University
Julien Chevallier: IPAG Business School (IPAG Lab)

Financial Innovation, 2024, vol. 10, issue 1, 1-50

Abstract: Abstract This study uses complex network analysis to investigate global stock market co-movement 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 artificial 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 financial centers of the developed, emerging, and frontier markets are identified. Using centrality indicators, we also illustrate changes in the importance of individual stock price indices during the COVID-19 pandemic. Based on these findings, we provide suggestions for investors and policy regulators to improve their international portfolios and strengthen their national financial risk preparedness.

Keywords: Complex network; Stock market co-movement; OHLC data; Degree centrality analysis (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1186/s40854-023-00548-5

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