Research by: Raymond Ka-Kay Pang, Oscar Granados, Harsh Chhajer, & Erika Fille T .Legara
As a response to the COVID-19 pandemic, most countries implemented various socio-economic policies and business restrictions almost simultaneously. An immediate consequence was an increase in yield rates for these nations. The resulting upward co-movement and upward movements in other yield rates explain the decrease in the mean correlation in bond dynamics, coinciding with the pandemic outbreak. Thus, understanding the dynamics of financial instruments in the Euro area is relevant to assess the increased economic strain from events seen in the last decade.
In this paper, we consider the movements of European sovereign bond yields for network filtering methods, where we focus on the COVID-19 period. We find that the impact of COVID-19 decreased the mean correlation, which was reflected within the normalized network length of all filtering methods. The network topology remained consistent with previous years, in which the trends between approaches were distinctive. The degree centrality was highest for GIIPS and ABFN countries when considering positive correlations and non-Euro countries within negatively correlated type networks. We identified the network structures of filtering methods within the COVID-19 period, which showed one large component consisting of GIIPS and ABFN countries for positive correlations. We were able to verify several of these relationships under an exponential random graph model, in which we find COVID-19 deaths to be significant within negatively correlated networks.
To cite this article: Pang, R. K., Granados, O., Chhajer, H. & Legara, E. F. T. (2021). An analysis of network filtering methods to sovereign bond yields during COVID-19. Physica A: Statistical Mechanics and its Applications, 574,125995. https://doi.org/10.1016/j.physa.2021.125995
To access this article: https://doi.org/10.1016/j.physa.2021.125995
About the Journal
Physica A: Statistical Mechanics and its Applications publishes research in the field of statistical mechanics and its applications. Statistical mechanics sets out to explain the behaviour of macroscopic systems, or the large scale, by studying the statistical properties of the microscopic or nanoscopic constituents. Applications of the concepts and techniques of statistical mechanics include: applications to physical and physiochemical systems such as solids, liquids and gases, interfaces, glasses, colloids, complex fluids, polymers, complex networks, applications to economic and social systems (e.g. socio-economic networks, financial time series, agent based models, systemic risk, market dynamics, computational social science, science of science, evolutionary game theory, cultural and political complexity), and traffic and transportation (e.g. vehicular traffic, pedestrian and evacuation dynamics, network traffic, swarms and other forms of collective transport in biology, models of intracellular transport, self-driven particles), as well as biological systems (biological signalling and noise, biological fluctuations, cellular systems and biophysics); and other interdisciplinary applications such as artificial intelligence (e.g. deep learning, genetic algorithms or links between theory of information and thermodynamics/statistical physics.).
Physica A: Statistical Mechanics and its Applications [ABS2]