Systemic risk in global volatility spillover networks: Evidence from option‐implied volatility indices

Abstract

With option-implied volatility indices, we identify networks of global volatility spillovers and examine time-varying systemic risk across global financial markets. The U.S. stock market is the center of the network and plays a dominant role in the spread of volatility spillover to other markets. The global systemic risks have intensified since the Federal Reserve exited from quantitative easing, hiked interest rate, and shrank its balance sheet. We further show that the U.S. monetary tightening is an important catalyst for the intensifying global systemic risk. Our findings highlight the pernicious effects of monetary tightening after an era of cheap money. K E Y W O R D S network, option-implied volatility, spillover, systemic risk

Quantitative Easing In Overcoming the Crisis: Criticism ofQeinthe Monetary System Of Capitalism

“…During these three phases, the Federal Reserve purchased $4.48 trillion worth of financial assets that boosted the money supply in the U.S. economy (Chapman & Kulkarni, 2020;Dinh et al, 2020;Fiebiger & Lavoie, 2020;Schlepper et al, 2020;Todorov, 2020;Sakir, 2015). Factually the policy uses three channels, among others (Kaminska & Zinna, 2020;Luck & Zimmermann, 2020;Yang et al, 2020;Zabala & Prats, 2020;Nam, 2018):…”

Preface

“…In addition, Xu and de Haan (2018) show that the relationship between reduced credit spreads -as a result of QE -and future employment growth weakened after the Fed introduced QE. Finally, using option implied volatility indices Yang and Zhou (2017) and Yang et al (2020) detect systemic risk spillover effects coming from US QE to the global economy. Overall, we can reasonably hold that financial shocks will be a permanent trait in the future as well, even more so if market economies will continue in the process of expanding financialisation of the latest decades.…”

Will a boom be followed by crash? A new systemic risk measure based on right-tail risk

“…Tail-dependence measurements are derived from the nonlinear dependence of tail losses across institutions or markets; examples of tail-dependence measurements include △CoVaR ( Adrian and Brunnermeier, 2016 ) and tail β ( Hartmann et al, 2005 ; Straetmans et al, 2008 ; De Jonghe, 2010 ). There are additional assessments, such as the CCA ( Gray and Malone, 2008 ), the volatility spillover network ( Diebold and Yılmaz, 2014 ; Yang et al, 2020 ), and the composite index ( Gray and Malone, 2008 ; Allen et al, 2012 ; Giglio et al, 2016 ; Nucera et al, 2016 ). One of the greatest advantages of reduced-form over structured-form measurements is that reduced-form measurements (which incorporate more abundant information) can characterize the dependence across financial markets or institutions in real time by using dynamic high-frequency financial market data.…”

“…First, the traditional systemic risk measure is concerned primarily with left-tail risk, focusing on direct losses arising from downward fluctuations of returns ( Acerbi and Tasche, 2002 ; Saunders and Allen, 2002 ; Adrian and Brunnermeier, 2016 ; Acharya et al, 2017 ; Brownlees and Engle, 2017 ), whereas we distinguish between left-tail and right-tail risk information and construct the LR-EGARCH. Second, in contrast to the volatility spillover literature ( Diebold and Yılmaz, 2014 ; Brunnermeier et al, 2020 ; Gong, et al, 2020; Yang et al, 2020 ), we focus on tail volatility and identify the spillover effects of various market states. Third, unlike the literature on jump volatility ( Jung and Maderitsch, 2014 ; Lahaye and Neely, 2020 ; Yang et al, 2021 ; Huang et al, 2022 ), we make two significant contributions: 1.…”