An analysis of network filtering methods to sovereign bond yields during COVID-19

Raymond Ka-Kay Pang; Oscar M Granados; Harsh Chhajer; Erika Fille T Legara

doi:10.1016/j.physa.2021.125995

An analysis of network filtering methods to sovereign bond yields during COVID-19

Physica A. 2021 Jul 15:574:125995. doi: 10.1016/j.physa.2021.125995. Epub 2021 Apr 9.

Authors

Raymond Ka-Kay Pang¹, Oscar M Granados², Harsh Chhajer³, Erika Fille T Legara⁴

Affiliations

¹ Department of Mathematics, London School of Economics and Political Science, Houghton Street, London WC2A 2AE, UK.
² Department of Economics and International Trade, Universidad Jorge Tadeo Lozano, Bogotá, Colombia.
³ Centre for Biosystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India.
⁴ Aboitiz School of Innovation, Technology, and Entrepreneurship, Asian Institute of Management, Makati, Philippines.

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.

Keywords: Crisis; Econophysics; Financial correlations; Sovereign bonds.