Dynamic Networks in Large Financial and Economic Systems

54 Pages Posted: 27 Jul 2020 Last revised: 24 Feb 2021

See all articles by Michael Ellington

Michael Ellington

University of Liverpool

Jozef Baruník

Charles University in Prague - Department of Economics; Institute of Information Theory and Automation, Prague

Date Written: July 14, 2020

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.

Keywords: Dynamic networks, Network connections, Time-varying parameter VAR, Realized Volatility, Connectedness, Spillovers.

JEL Classification: C10, C40, C55, C58, G00

Suggested Citation

Ellington, Michael and Barunik, Jozef, Dynamic Networks in Large Financial and Economic Systems (July 14, 2020). Available at SSRN: https://ssrn.com/abstract=3651134 or http://dx.doi.org/10.2139/ssrn.3651134

Michael Ellington (Contact Author)

University of Liverpool ( email )

Chatham Street
Liverpool, L69 7ZA
United Kingdom

Jozef Barunik

Charles University in Prague - Department of Economics ( email )

Opletalova 26
Prague 1, 110 00
Czech Republic

HOME PAGE: http://ies.fsv.cuni.cz/en/staff/barunik

Institute of Information Theory and Automation, Prague ( email )

Pod vodarenskou vezi 4
CZ-18208 Praha 8
Czech Republic

HOME PAGE: http://staff.utia.cas.cz/barunik/home.htm

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