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Measuring interconnectedness between financial institutions with Bayesian time-varying vector autoregressions

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  • Marco Valerio Geraci
  • Jean-Yves Gnabo

Abstract

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.
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  • Marco Valerio Geraci & Jean-Yves Gnabo, 2015. "Measuring interconnectedness between financial institutions with Bayesian time-varying vector autoregressions," Working Papers ECARES 2015-51, ULB -- Universite Libre de Bruxelles.
  • Handle: RePEc:eca:wpaper:2013/222092
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    Cited by:

    1. Jouchi Nakajima, 2020. "Discussion of “Bayesian forecasting of multivariate time series: scalability, structure uncertainty and decisions”," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(1), pages 33-36, February.
    2. Y'erali Gandica & Marco Valerio Geraci & Sophie B'ereau & Jean-Yves Gnabo, 2017. "Fragmentation, integration and macroprudential surveillance of the US financial industry: Insights from network science," Papers 1707.00296, arXiv.org, revised Jan 2018.
    3. Melle Bijlsma & Malka de Castro Campos & Raymond Chaudron & David-Jan Jansen, 2019. "Building a multilayer macro-network for the Netherlands: A new way of looking at financial accounts and international investment position data," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Are post-crisis statistical initiatives completed?, volume 49, Bank for International Settlements.
    4. Yerali Gandica & Marco Valerio Geraci & Sophie Béreau & Jean-Yves Gnabo, 2018. "Fragmentation, integration and macroprudential surveillance of the US financial industry: Insights from network science," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-23, April.
    5. Jozef Barunik & Michael Ellington, 2020. "Dynamic Networks in Large Financial and Economic Systems," Papers 2007.07842, arXiv.org, revised Feb 2021.
    6. Billio Monica & Casarin Roberto & Costola Michele & Iacopini Matteo, 2021. "COVID-19 spreading in financial networks: A semiparametric matrix regression model," Papers 2101.00422, arXiv.org.
    7. So Jung Hwang & Hyunduk Suh, 2021. "Analyzing Dynamic Connectedness in Korean Housing Markets," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(2), pages 591-609, January.
    8. Jozef Barunik & Mattia Bevilacqua & Radu Tunaru, 2018. "Asymmetric Network Connectedness of Fears," Papers 1810.12022, arXiv.org, revised Oct 2020.
    9. Kumar, Sudarshan & Bansal, Avijit & Chakrabarti, Anindya S., 2019. "Ripples on financial networks," IIMA Working Papers WP 2019-10-01, Indian Institute of Management Ahmedabad, Research and Publication Department.
    10. Samitas, Aristeidis & Kampouris, Elias & Kenourgios, Dimitris, 2020. "Machine learning as an early warning system to predict financial crisis," International Review of Financial Analysis, Elsevier, vol. 71(C).
    11. Lubos Hanus & Lukas Vacha, 2018. "Time-Frequency Response Analysis of Monetary Policy Transmission," Working Papers IES 2018/30, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Oct 2018.
    12. Chen, Yanhua & Li, Youwei & Pantelous, Athanasios & Stanley, Eugene, 2020. "Short-run disequilibrium adjustment and long-run equilibrium in the international stock markets: A network-based approach," MPRA Paper 101700, University Library of Munich, Germany.
    13. Chan, Joshua C.C. & Eisenstat, Eric & Strachan, Rodney W., 2020. "Reducing the state space dimension in a large TVP-VAR," Journal of Econometrics, Elsevier, vol. 218(1), pages 105-118.
    14. Jozef Barunik & Michael Ellington, 2020. "Dynamic Network Risk," Papers 2006.04639, arXiv.org, revised Jul 2020.
    15. Jean-Baptiste Hasse, 2020. "Systemic Risk: a Network Approach," Working Papers halshs-02893780, HAL.
    16. Boeckelmann Lukas & Stalla-Bourdillon Arthur, 2021. "Structural Estimation of Time-Varying Spillovers: An Application to International Credit Risk Transmission," Working papers 798, Banque de France.
    17. Aromi, Daniel & Clements, Adam, 2019. "Spillovers between the oil sector and the S&P500: The impact of information flow about crude oil," Energy Economics, Elsevier, vol. 81(C), pages 187-196.
    18. Jean-Baptiste Hasse, 2020. "Systemic Risk: a Network Approach," AMSE Working Papers 2025, Aix-Marseille School of Economics, France.
    19. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2020. "Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 13(4), pages 1-23, April.
    20. Y'erali Gandica & Sophie B'ereau & Jean-Yves Gnabo, 2019. "A multilevel analysis to systemic exposure: insights from local and system-wide information," Papers 1910.08611, arXiv.org.

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    More about this item

    Keywords

    financial interconnectedness; time-varying parameter; granger casuality;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • 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
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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