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

Monica Billio, Roberto Casarin, Michele Costola and Matteo Iacopini

Econometrics and Statistics, 2024, vol. 29, issue C, 113-131

Abstract: Network models represent a useful tool to describe the complex set of financial relationships among heterogeneous firms in the system. A new Bayesian semiparametric model for temporal multilayer networks with both intra- and inter-layer connectivity is proposed. 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 number of COVID-19 cases in Europe. Two layers, defined by stock returns and volatilities are considered and within and between layers connectivity is investigated. The financial connectedness arising from the interactions between two layers is measured. The model is applied in order to compare 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)
Date: 2024
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Related works:
Working Paper: COVID-19 spreading in financial networks: A semiparametric matrix regression model (2021)
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:eee:ecosta:v:29:y:2024:i:c:p:113-131

DOI: 10.1016/j.ecosta.2021.10.003

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