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

Monica Billio, Roberto Casarin, Costola Michele and Iacopini Matteo

Papers from arXiv.org

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.

Date: 2021-01
New Economics Papers: this item is included in nep-net
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Citations: View citations in EconPapers (5)

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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:arx:papers:2101.00422

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