In this paper, we adopt a conditional tail risk network methodology for the assessment of transmission channels of risk. In particular, we employ weighted and directed networks to model the mutual influence between banks, with the weights being linked to tail risk measures. More specifically, in the empirical comparison we focus on MES-based pairwise dependence networks and on the parametric \(\Delta \) CoVaR network estimated on ARMA(1,1)-GARCH(1,1) residuals through SCAD-penalized quantile regression. We use different network indicators to investigate the importance of a bank’s role in both spreading and absorbing risk from other financial institutions. Our analysis focuses on a sample based on banks included in the European Banking Authority 2023 stress test, complemented by two additional systemically relevant European institutions and we consider daily data for the period 2015–2024. The analyses are conducted over four economically meaningful subperiods in order to evaluate the evolution of systemic interconnections across different macro-financial regimes. We find that the choice of tail risk measure leads to substantial differences in network topology, centrality rankings, and community composition. The empirical results demonstrate significant variations in the structural characteristics of the networks through time.
