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Dynamic Networks in Large Financial and Economic Systems

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  • Jozef Barunik
  • Michael Ellington

Abstract

This paper identifies frequency-dependent network structures that evolve over time. To measure such dynamic networks, we propose a computationally efficient framework that is widely applicable to many economic and financial datasets, and readily available for high dimensional models. We provide Monte Carlo evidence that our measures are able to reliably recover true network connections from a battery of DGPs and also develop a testing procedure for statistical differences among frequency-dependent network connections. Our empirical application on firm-level realized volatilities documents substantial heterogeneities in dynamic network structures that may be useful as an online monitoring tool to help guide macro-prudential policy.

Suggested Citation

  • Jozef Barunik & Michael Ellington, 2020. "Dynamic Networks in Large Financial and Economic Systems," Papers 2007.07842, arXiv.org, revised Feb 2021.
  • Handle: RePEc:arx:papers:2007.07842
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    References listed on IDEAS

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    Cited by:

    1. Mykola Babiak & Jozef Barunik, 2021. "Currency Network Risk," Papers 2101.09738, arXiv.org, revised Jul 2021.
    2. Jozef Barunik & Mattia Bevilacqua & Robert Faff, 2021. "Dynamic industry uncertainty networks and the business cycle," Papers 2101.06957, arXiv.org, revised Mar 2021.

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