Friday, 26 February 2021 20:17

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

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Billio M, Casarin R, Costola M, Iacopini M

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  • Published in: Billio M., Casarin R., Costola M., Iacopini M., Working Paper Series - Department of Economics of the Ca’ Foscari University of Venice (ISSN 1827-3580), Department of Economics, University of Venice "Ca' Foscari, vol. 2021/05, pp. 1-34 
  • Year: 2021
  • 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.
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Monica Billio

Full Professor in Econometrics - Ca’ Foscari University of Venice, Department of Economics

Promoting partner, Scientific Committees and Director of the Area Data Science

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