In this work, we investigate the impact of the COVID-19 pandemic on sovereign bond yields amongst European countries . 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 as an indicator of the economic health of countries . We find that the average correlation between sovereign bonds within the  COVID-19 period decreases, from the peak observed in the 2019-2020 period, where this trend is also reflected in all network filtering methods . We also find variations between the movements of different network filtering methodsunder various network measures .
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 .
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amongst European countries
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as an indicator of the economic health of countries
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that the average correlation between sovereign bonds within
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mean correlation to decrease across all filtering methods during the
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decreases, from the peak observed in the 2019-2020 period, where this trend is also reflected in all network filtering methods . We also find variations between the movements of different network filtering methodsunder various network measures
. 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