🤖 AI Summary
Conventional network connectivity measures fail to capture dynamic spillovers both across and within markets under clustered structures. Method: We propose a cluster-based network connectivity framework that partitions nodes by asset class, industry, or region; assumes orthogonal shocks across clusters but correlated shocks within clusters—relaxing the standard VAR orthogonalization assumption. Our approach is the first to embed cluster structure into the variance decomposition framework, integrating Sims orthogonalization with Koop-Pesaran-Potter-Shin generalized impulse responses to jointly achieve cross-cluster orthogonal identification and intra-cluster generalized identification. Node ranking depends solely on inter-cluster relationships, rendering intra-cluster ordering irrelevant. Results: Applied to equity market data from sixteen countries, the framework uncovers heterogeneous connectivity patterns under regional and industry clustering, substantially enhancing the explanatory power and predictive accuracy of systemic risk transmission pathways.
📝 Abstract
Network connections, both across and within markets, are central in countless economic contexts. In recent decades, a large literature has developed and applied flexible methods for measuring network connectedness and its evolution, based on variance decompositions from vector autoregressions (VARs), as in Diebold and Yilmaz (2014). Those VARs are, however, typically identified using full orthogonalization (Sims, 1980), or no orthogonalization (Koop, Pesaran, and Potter, 1996; Pesaran and Shin, 1998), which, although useful, are special and extreme cases of a more general framework that we develop in this paper. In particular, we allow network nodes to be connected in"clusters", such as asset classes, industries, regions, etc., where shocks are orthogonal across clusters (Sims style orthogonalized identification) but correlated within clusters (Koop-Pesaran-Potter-Shin style generalized identification), so that the ordering of network nodes is relevant across clusters but irrelevant within clusters. After developing the clustered connectedness framework, we apply it in a detailed empirical exploration of sixteen country equity markets spanning three global regions.