This paper proposes an approach to measuring tail risk connectedness by leveraging spatial methods, particularly the spatial autoregressive model, applied to value-at-risk (VaR) estimates. Departing from traditional methods reliant on volatility spillovers, our approach aims to capture the tail dimension of market interconnectedness, offering insights beyond conventional metrics. Through an empirical analysis, we demonstrate the efficacy of the proposed approach in assessing systemic risks, providing a valuable tool for policymakers, investors, and financial regulators.