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

ArXiv ID: 2502.15458 “View on arXiv”

Authors: Unknown

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.

Keywords: Network Connectedness, Vector Autoregression (VAR), Asset Pricing, Global Equity Markets, Shock Transmission

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper introduces a sophisticated clustered VAR framework with extensive LaTeX derivations for impulse response and variance decomposition identification (high math). It then applies this framework in a detailed empirical study of 16 global equity markets using standard econometric software and likely extensive data processing, demonstrating backtest-ready implementation (high rigor).

  flowchart TD
    A["Research Goal<br>Develop & apply new framework<br>for measuring network connectedness"] --> B["Methodology Development<br>Clustered Connectedness Framework"]
    
    B --> C["Data/Inputs<br>16 Country Equity Markets<br>3 Global Regions"]
    
    C --> D["Computational Process<br>1. Estimate VAR models<br>2. Apply Clustered Identification<br>3. Calculate Variance Decompositions"]
    
    D --> E["Key Findings/Outcomes<br>1. Framework allows<br>intra-cluster correlation<br>2. Inter-cluster orthogonality<br>3. Ordering irrelevant within clusters<br>4. Superior network measurement<br>5. Application to global equity markets"]
    
    style A fill:#e1f5fe
    style B fill:#f3e5f5
    style C fill:#e8f5e8
    style D fill:#fff3e0
    style E fill:#fce4ec