We examine the impact of COVID-19 to sovereign bond yields compared with previous financial events.
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We use filtering methods to analyze the time series of sovereign bond yields under network measures.
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We incorporate economic and health variables within an exponential random graph model for COVID-19 period correlated networks.
Abstract
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.
The authors thank the Net-COVID workshop series organized by the University of Maryland COMBINE program and the University of Vermont’s Complex System Center.
