COVID-19 spreading in financial networks: A semiparametric matrix regression model

Monica Billio, Roberto Casarin, Michele Costola () and Matteo Iacopini ()
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Michele Costola: Department of Economics, Ca' Foscari University of Venice
Matteo Iacopini: Vrije Universiteit Amsterdam

No 2021:05, Working Papers from Department of Economics, University of Venice "Ca' Foscari"

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: C11 C58 G10 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2021
New Economics Papers: this item is included in nep-ecm, nep-net, nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Journal Article: COVID-19 spreading in financial networks: A semiparametric matrix regression model (2024)
Working Paper: COVID-19 spreading in financial networks: A semiparametric matrix regression model (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:ven:wpaper:2021:05

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