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Multidimensional risk connectedness among global systemically important financial institutions: A multilayer spillover network analysis

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  • Chen, Ning
  • Li, Shaofang
  • Tian, Sihua
  • Lu, Shuai

Abstract

Using daily stock data from global systemically important financial institutions (G-SIFIs), this study constructs a multilayer network based on the Diebold-Yilmaz (DY) connectedness indicators and a nonlinear Granger causality test to investigate the risk spillover and interconnectedness among G-SIFIs for return, volatility, and extreme risk. The results show that the extreme risk layer exhibits a stronger spillover effect than the return and volatility layers, particularly during risk periods. The banking sector displays more substantial internal risk spillovers than the insurance sector and predominantly acts as a net risk emitter in both the return and extreme risk layers. During the European debt crisis (2011–2012) and the COVID-19 (2020), certain G-SIFIs from the USA and Europe play pivotal roles in the contagion of systemic risk. Additionally, the extreme risk and volatility layers tend to exhibit more cross-layer connections than those between other layers.

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  • Chen, Ning & Li, Shaofang & Tian, Sihua & Lu, Shuai, 2025. "Multidimensional risk connectedness among global systemically important financial institutions: A multilayer spillover network analysis," Economic Analysis and Policy, Elsevier, vol. 88(C), pages 529-556.
    • Handle: RePEc:eee:ecanpo:v:88:y:2025:i:c:p:529-556
    DOI: 10.1016/j.eap.2025.09.016
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    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • F3 - International Economics - - International Finance
    • F5 - International Economics - - International Relations, National Security, and International Political Economy
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