Spatial spillover effects and risk contagion around G20 stock markets based on volatility network

A Weighted and Directed Perspective of Global Stock Market Connectedness: A Variance Decomposition and GERGM Framework

“…Economic globalization and financial liberalization, accompanied by the accelerated international capital flows, cross-border investments and speculative activities, have transformed the global stock markets from a state of fragmentation into a state of mutual influence and overall movement. The increasing financial penetration among countries has proven to be double-edged, which not only assists in the optimization of investment portfolios and reduction of local risks, but also induces market co-movements and consequently raises the likelihood of risk contagion across economies [1]. The repercussions of contagion are at the core of systemic risk, which depends considerably on the carrier of connectedness among financial institutions or financial markets [2,3].…”

“…One stream of literature focuses on financial networks. The inner logic of financial networks is that the intricate connectedness, either physical based on bilateral exposures/flows between financial institutions, or association-based depicting return dependency among financial markets, could be captured and analyzed in complex financial systems using a network approach [1,2,6]. The network approach, which describes relationship architecture and regularities involved in complex multivariate systems, has become a powerful tool in financial crises early warning and tracking [7,8], risk spillover sources tracing [9,10], or exploitation of asset allocation [11,12].…”

“…The network approach, which describes relationship architecture and regularities involved in complex multivariate systems, has become a powerful tool in financial crises early warning and tracking [7,8], risk spillover sources tracing [9,10], or exploitation of asset allocation [11,12]. Three research paradigms exist in the current financial network literature [1], namely, (i) mean-spillover network or Granger-causality network [13], (ii) volatility spillover network represented by variance decomposition-based network [14] and GARCH-based network [15], and (iii) risk spillover network with the main forms in tail-risk driven network [16] and extreme risk network [8]. These three overarching frameworks have enlightened many empirical works.…”

“…Another strand of literature, which addresses the second research question, relates to the quantification of the spillover channels across markets. Scholars seek to explain the influential factors that amplify or mitigate volatility spillover in terms of equity market importance, development and liquidity [10,[21][22][23], investment behaviors and macroeconomic fundamentals [1,20], capital account flow [24,25], financial crisis [20,26,27], and financial institution linkages [3,28]. For instance, Baumöhl et al [10] used spatial regression to model the volatility spillover network of 40 developed, emerging, and frontier stock markets between 2006 and 2014 to identify escalating magnitude of spillovers with increasing market size, market liquidity, and economic openness.…”

“…For instance, Baumöhl et al [10] used spatial regression to model the volatility spillover network of 40 developed, emerging, and frontier stock markets between 2006 and 2014 to identify escalating magnitude of spillovers with increasing market size, market liquidity, and economic openness. Applying the same method in the G20 stock network, Zhang et al [1] found that government debt and inflation are positively correlated with spillover risk. Most of the existing literature uses linear regression, ordinary panel regression, and spatial autoregressive models, which do not deal with the correlation problem embedded in relational data [5].…”