COVID-19 Spreading in Financial Networks: A Semiparametric Matrix Regression Model
33 Pages Posted: 12 Jan 2021
Date Written: January 8, 2021
Network models represent a useful tool to describe the complex set of financial relationships among heterogeneous firms in the system. In this paper, we propose a new semiparametric model for temporal multilayer causal networks with both intra- and inter-layer connectivity. A Bayesian model with a hierarchical mixture prior distribution is assumed to capture heterogeneity in the response of the network edges to a set of risk factors including the European COVID-19 cases. We measure the financial connectedness arising from the interactions between two layers defined by stock returns and volatilities. In the empirical analysis, we study the topology of the network before and after the spreading of the COVID-19 disease.
Keywords: Multilayer networks, financial markets, COVID-19
JEL Classification: C11, C58, G10
Suggested Citation: Suggested Citation