The coronavirus disease 2019 (COVID-19) pandemic has led to tremendous loss of human life and has severe social and economic impacts worldwide. The spread of the disease has also caused dramatic uncertainty in financial markets, especially in the early stages of the pandemic. In this paper, we adopt the stochastic actor-oriented model (SAOM) to model dynamic/longitudinal financial networks with the covariates constructed from the network statistics of COVID-19 dynamic pandemic networks. Our findings provide evidence that the transmission risk of the COVID-19, measured in the transformed pandemic risk scores, is a main explanatory factor of financial network connectedness from March to May 2020. The pandemic statistics and transformed pandemic risk scores can give early signs of the intense connectedness of the financial markets in mid-March 2020. We can make use of the SAOM approach to predict possible financial contagion using pandemic network statistics and transformed pandemic risk scores of the COVID-19 and other pandemics.

中文翻译：

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COVID-19 对金融网络演化影响的随机面向参与者建模

2019 年冠状病毒病 (COVID-19) 大流行已导致巨大的人类生命损失，并在全球范围内产生了严重的社会和经济影响。这种疾病的传播也给金融市场带来了巨大的不确定性，尤其是在大流行的早期阶段。在本文中，我们采用随机参与者导向模型 (SAOM) 来建模动态/纵向金融网络，协变量由 COVID-19 动态大流行网络的网络统计数据构建。我们的研究结果提供证据表明，以转换后的大流行风险评分衡量的 COVID-19 传播风险是 2020 年 3 月至 5 月金融网络连通性的主要解释因素。大流行统计数据和转换后的大流行风险评分可以提供早期迹象2020 年 3 月中旬金融市场的紧密联系。