The importance of heterogeneity in dynamic network models applied to European systemic risk - Xingmin Zhang; Anne Opschoor; André Lucas (Tinbergen Institute, 2021)
Standard spatial time-series models for financial networks can fail substantially in uncovering empirical network and risk dynamics. We propose a novel empirical spatial modeling framework that solves this problem by accommodating both heterogeneity and time-variation in economic connections and spillovers. While highly flexible, the model is still straightforward to estimate. We apply the model to several datasets for Eurozone sovereign credit risk during the sovereign debt crisis. Accounting for heterogeneity and time-variation turns out to be empirically important and the new model uncovers intuitive patterns that would go unnoticed otherwise in currently available homogeneous and/or static spatial financial network models.
Keywords: 330; dynamic networks; spatial auto-regressions; heterogeneous spatial contagion; network heterogeneity; sovereign risk dynamics