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

Monica Billio; Roberto Casarin; Michele Costola; Matteo Iacopini

doi:10.1016/j.ecosta.2021.10.003

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

Monica Billio, Roberto Casarin, Michele Costola, Matteo Iacopini

Research output: Contribution to Journal › Article › Academic › peer-review

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.

Original language	English
Pages (from-to)	113-131
Number of pages	19
Journal	Econometrics and Statistics
Volume	29
DOIs	https://doi.org/10.1016/j.ecosta.2021.10.003
Publication status	Published - 2024

Funding

We are grateful to the Editor and two anonymous reviewers for the comments that helped improving the earlier draft of the manuscript. We thank the participants at the 14th International Conference Computational and Financial Econometrics (CFE 2020) for the useful comments. We also thank Maurizio La Mastra for the excellent research assistance. This research used SCSCF and the HPC multiprocessor cluster systems provided by the Venice Centre for Risk Analytics (VERA) at University Ca’ Foscari of Venice. Monica Billio, Roberto Casarin, and Michele Costola acknowledge financial support from the Italian Ministry MIUR under the PRIN project Hi-Di NET - Econometric Analysis of High Dimensional Models with Network Structures in Macroeconomics and Finance (grant agreement no. 2017TA7TYC) and from the Venice Centre for Risk Analytics (VERA) at the University Ca’ Foscari of Venice. Matteo Iacopini acknowledges financial support from the Marie Skłodowska-Curie Actions, European Union, Seventh Framework Program HORIZON 2020 under REA grant agreement n. 887220.

Funders	Funder number
Italian Ministry MIUR	2017TA7TYC
SCSCF
Venice Centre for Risk Analytics
Horizon 2020 Framework Programme
H2020 Marie Skłodowska-Curie Actions
Università Ca' Foscari Venezia
European Commission
Research Executive Agency	887220

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.ecosta.2021.10.003

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2 Citations
1 Working paper

COVID-19 spreading in financial networks: A semiparametric matrix regression model
Billio, M., Casarin, R., Costola, M. & Iacopini, M., 2020, (arXiv.org).
Research output: Working paper / Preprint › Working paper › Professional

Cite this

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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.",

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JO - Econometrics and Statistics

JF - Econometrics and Statistics

ER -

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

Abstract

Funding

UN SDGs

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Research output

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

Cite this