Clustered Network Connectedness: A New Measurement Framework with Application to Global Equity Markets

TL;DR

This paper introduces a novel clustered network connectedness framework that captures both inter- and intra-cluster relationships, applied to analyze global equity markets, advancing the measurement of financial interconnectedness.

Contribution

It develops a new framework allowing for clustered network connections with orthogonalized inter-cluster shocks and correlated intra-cluster shocks, extending existing connectedness measurement methods.

Findings

Reveals significant inter-cluster connectedness among global markets.

Identifies intra-cluster shock correlations within regions.

Provides detailed empirical insights into global equity market dynamics.

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.