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Connectedness and systemic importance of global financial markets: A multilayer network perspective

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  • Hu, Yunchao
  • Wang, Gang-Jin
  • Gao, Wenyu
  • Lu, Guibin
  • Uddin, Gazi Salah

Abstract

We propose a multilayer network modeling method to study the connectedness and systemic importance of global financial markets (including stock, foreign exchange (FX), and bond markets). We investigate the connectedness with various levels by constructing multilayer global financial networks, and further measure the systemic importance of economies by multilayer centrality indices from the information transmission perspective. We finally examine the structure changes of multilayer networks before, during, and after major crisis events. We find that: (i) stock markets mainly transmit information to other markets, and FX markets receive larger cross-market information; (ii) stock markets in larger or more mature market economies are more likely to be stable sources of cross-market information transmission; (iii) the Euro-American developed market economies typically serve as stable pivotal economies; and (iv) the COVID-19 pandemic had a greater impact on global financial markets as an exogenous shock compared to the Brexit referendum.

Suggested Citation

  • Hu, Yunchao & Wang, Gang-Jin & Gao, Wenyu & Lu, Guibin & Uddin, Gazi Salah, 2026. "Connectedness and systemic importance of global financial markets: A multilayer network perspective," Research in International Business and Finance, Elsevier, vol. 84(C).
    • Handle: RePEc:eee:riibaf:v:84:y:2026:i:c:s0275531926000632
    DOI: 10.1016/j.ribaf.2026.103336
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    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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