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.

中文翻译：

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基于日内开盘-高-低收盘数据对 COVID-19 大流行期间全球股市联动进行复杂网络分析

本研究使用复杂的网络分析来调查 2019 年冠状病毒病 (COVID-19) 大流行黑天鹅事件期间全球股市的联动。我们提出了一种基于开盘-高-低-收盘（OHLC）数据计算股票价格指数相关性的新方法。与广泛使用的基于收益的方法相比，可以利用更多的日内信息。使用假设检验来选择网络中包含的边，以避免严格设置人为阈值。研究人员检查了在 COVID-19 爆发之前（2017-2019 年）和之后（2020-2022 年）使用 70 个重要的全球股票价格指数构建的全球股票市场复杂网络的拓扑。证据表明，基于 OHLC 数据的全球股票价格指数复杂网络的度中心性比基于收益的网络具有更好的幂律分布特征。揭示全球股市联动特征，确定发达市场、新兴市场和前沿市场的金融中心。使用中心性指标，我们还说明了 COVID-19 大流行期间个股价格指数重要性的变化。基于这些发现，我们为投资者和政策监管机构提供建议，以改善他们的国际投资组合并加强他们的国家金融风险准备。