In this work, we investigate the impact of the COVID-19 pandemic on sovereign bond yields. We consider the temporal changes from financial correlations using network filtering methods. These methods consider a subset of links within the correlation matrix, which gives rise to a network structure. We use sovereign bond yield data from 17 European countries between the 2010 and 2020 period. We find the mean correlation to decrease across all filtering methods during the COVID-19 period. We also observe a distinctive trend between filtering methods under multiple network centrality measures. We then relate the significance of economic and health variables towards filtered networks within the COVID-19 period. Under an exponential random graph model, we are able to identify key relations between economic groups across different filtering methods.

在这项工作中，我们调查了 COVID-19 大流行对主权债券收益率的影响。我们使用网络过滤方法考虑金融相关性的时间变化。这些方法考虑相关矩阵中的链接子集，从而产生网络结构。我们使用 2010 年至 2020 年间 17 个欧洲国家的主权债券收益率数据。我们发现在 COVID-19 期间，所有过滤方法的平均相关性都在降低。我们还观察到多种网络中心性度量下过滤方法之间的明显趋势。然后，我们将经济和健康变量的重要性与 COVID-19 期间的过滤网络联系起来。在指数随机图模型下，我们能够通过不同的过滤方法识别经济群体之间的关键关系。