  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.
