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Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying Vector Autoregressions

  • Marco Valerio Geraci and Jean-Yves Gnabo


We propose a market-based framework that exploits time-varying parameter vector autoregressions to estimate the dynamic network of financial spillover effects. We apply it to financials in the Standard & Poor’s 500 index and estimate interconnectedness at the sectoral and institutional levels. At the sectoral level, we uncover two main events in terms of interconnectedness: the Long-Term Capital Management crisis and the 2008 financial crisis. After these crisis events, we find a gradual decrease in interconnectedness, not observable using the classical rolling-window approach. At the institutional level, our framework delivers more stable interconnectedness rankings than other comparable market-based measures.


Corresponding author

* Geraci (corresponding author),, University of Cambridge INET Institute and Université Libre de Bruxelles ECARES; Gnabo,, University of Namur CeReFiM and University of Paris Nanterre EconomiX-CNRS.


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We thank Tobias Adrian (the referee), Matteo Barigozzi, Christiane Baumeister, Sophie Béreau, Oscar Bernal, Monica Billio, Christian Brownlees, Alain de Combrugghe, Frank Diebold, Michele Lenza, Matteo Luciani, Paul Malatesta (the editor), David Veredas, and Kamil Yılmaz and conference and seminar participants at the 2016 French Finance Association Conference, the 2016 Conference on Financial Risk and Network Theory, the 2016 European Economic Association Meeting, Fordham University, Université Libre de Bruxelles, University of Mannheim, and Université de Namur for valuable comments. We also gratefully acknowledge financial support from the Communauté Française de Belgique. We are responsible for all remaining errors and omissions.



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Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying Vector Autoregressions

  • Marco Valerio Geraci and Jean-Yves Gnabo


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