Another Look into Tail Risk Connectedness Using Network Modelling: Evidence from European Stock Markets

This paper proposes an approach for measuring tail risk connectedness in financial networks by leveraging spatial methods, particularly the spatial autoregressive model, applied to Value-at-Risk (VaR) and Expected Shortfall (ES) estimates. Given the definition of the network structure, and departing from traditional methods reliant on volatility spillovers, our approach aims to capture the effect of the nature of the event on the tail dimension of market interconnectedness, offering insights beyond conventional metrics. Our results show that since the nature of the event substantially affects the convergence (divergence) of the response of the agents of the financial network to a shock, accounting for this effect is crucial to correctly measure its transmission. Since not all shocks are similar, events such as the Great Recession or the COVID-19 pandemic do not affect the tail risk of the financial network in a similar way, even when volatility spillovers increase in both cases. The relevance of considering the source of the shock is shown through an empirical analysis of the most important European stock markets, demonstrating the efficacy of the proposed approach in assessing systemic risks, therefore providing a valuable tool for policymakers, investors, and financial regulators.