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

Abstract

The financial penetration accelerated by economic globalization and financial liberalization has inevitably induced market co-movement and the rising likelihood of cross-market risk contagion. An in-depth analysis concerning the carrier of risk contagion, i.e., market connectedness network, is of great significance for risk management. This study aims to establish a holistic framework to shed light on the topological dynamics and the evolving channels of connectedness network among 24 major stock markets in two aspects; namely, a dynamic perspective juxtaposing crisis and non-crisis periods, and a contrasting perspective between risk absorption and risk spillover. To this end, a methodological framework of the generalized variance decomposition and generalized exponential random graph models (GERGM) is constructed, in which the former method formulates the asymmetric causal relationships of stock return volatility among countries and regions into weighted and directed networks, and the latter method simulates and models the varying attributes of different contagion channels in the formation of tie directions and weights. The results indicate that the global stock market network reflects typical event-driven and time-varying characteristics. Countries and regions that rely heavily on foreign direct investment (FDI) are more likely to absorb risks, especially during the post-crisis recovery period, while countries and regions with higher foreign portfolio holdings are more inclined to risk spillover, especially during the subprime crisis. Geographical proximity and bilateral trade volume amplify risk contagion, whereas foreign exchange reserve holding improves robustness. This holistic framework allows the identification of the direction and intensity of risk contagion and the clarification of priority of risk transmission channels in different stages, thus reducing the uncertainty of risk management and providing insights into the macro-prudential managements toward sustainable economic development.

COVID-19 related media sentiment and the yield curve of G-7 economies

“…Our findings are consistent with those of Youssef et al (2021) , who examine the spillover effects of the EPU index on indices of eight major stock markets. While the existing literature attributed to the heightened and interconnected nature of global stock markets during times of market distress, as opined by Zhang et al, 2020 , Zhang et al, 2020 and Cepoi (2020), our findings extend this literature in the fixed income markets domain by specifically analyzing the impact of the pandemic across the entire yield curve. Thus, we are able to isolate the impact of the pandemic for various investment horizons ranging from short-term, medium term to long term.…”

“…Apart from MCI, US appears to be the main transmitter of spillover to all other markets. Our finding again underscores the central role of US in the global financial architecture and thus have a central role as a transmitter of risk spillovers ( Bissoondoyal-Bheenick et al, 2020 , Zhang et al, 2020 , Zhang et al, 2020 ). Although the Pandemic is different from the global financial crisis (GFC) which originated from USA, yet the role of US as a main transmitter of spillover is consistent.…”

Risk Connectedness among International Stock Markets: Fresh Findings from a Network Approach

“…These results indicate that the European stock markets are less affected by spillover effects from other stock markets and confirm that the German stock market has the greatest influence on other stock markets. This finding aligns with the results of Youssef [14] and Zhang [78,79]. Thus, the developed stock markets continued to be net transmitters of risk during the sample periods, except for SW and CA, whereas Asian stock markets were net receivers.…”

Portfolio Construction: A Network Approach

“…In addition, another interesting pattern concerning crises is that network density is increasing, especially in Transfer Entropy network (Remark 7). This finding is supported by the relevant literature [30,[68][69][70] and it is intuitively expected, because the massive sell-off in the stock market causes stocks to move in a correlated manner [67].…”

“…In times of crisis, network density increases (Figures 12-14); this is understandable because the massive sell-off in the stock market causes stocks to move in a correlated manner [67]. This finding is supported by the relevant literature [68][69][70]. More specifically, we found that network density is increasing due to two reasons, namely the increase of the number of links (unweighted network, Figure 12) and the increase of the value of weights (distribution of weights, Figure 15).…”