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Dynamic Bayesian Network Analysis of Global Financial Markets Interdependence

Leandro Coghi Bernardelli et al.

Computational Economics2026https://doi.org/10.1007/s10614-025-11203-9article
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Abstract

This study investigates how global financial markets interact dynamically between January 3, 2000, and June 23, 2023, focusing on asset connections during crisis and non-crisis periods. We utilized Dynamic Bayesian Networks (DBNs) to map the complex interactions among assets, analyzing both their individual characteristics and the overall network structure. The results reveal that, over time, financial assets form stronger connections with those located in geographically proximate regions, both during crisis and non-crisis periods. While this pattern does not provide conclusive evidence, it does suggest signs of a potential “deglobalization” process, marked by geopolitical fragmentation and a gradual shift toward regionalization, particularly influenced by the growing role of emerging economic hubs such as Asia. These findings emphasize the need for investors to adopt more regionally focused strategies tailored to local contexts, with increased presence on the spot (“ in loco ”). Additionally, econometric analysis confirmed modularity ( Modul ) as a critical factor, demonstrating that geographically proximate assets form communities that significantly influence financial returns.

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https://doi.org/https://doi.org/10.1007/s10614-025-11203-9

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@article{leandro2026,
  title        = {{Dynamic Bayesian Network Analysis of Global Financial Markets Interdependence}},
  author       = {Leandro Coghi Bernardelli et al.},
  journal      = {Computational Economics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s10614-025-11203-9},
}

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