Measuring the network connectedness of global stock markets

Chen, Gong; Tang, Pan; Wang, Yutong

doi:10.1016/j.physa.2019.122351

Cited by 37 publications

(20 citation statements)

References 12 publications

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“…The developed markets are more influential and less sensitive to external shocks than emerging markets, thus the strength of risk transmitted from developed economies to emerging markets is generally greater than the reverse linkages. These results are in line with the findings of previous studies [1,18,44,51]. In addition, we identify France as, on average, the largest volatility sender, and the UK as the largest risk receiver on average, which corroborates Yarovaya et al [57] on the role of the UK but differs from Yarovaya et al [57] and Liu et al [15] on the risk sender.…”

Section: Main Findingssupporting

confidence: 92%

“…The network density reflects the closeness of the connections between the nodes, and the clustering coefficient is used to describe the degree of network grouping. The density and clustering coefficients of both crisis networks are larger than the stationary and recovery periods, indicating closer connections between nodes and more concentrated distribution during the crisis periods [44]. The financial crisis will change the fundamental statistical indicators of the networks, and this structural change will further accelerate the course and amplify the consequences of the crisis.…”

Section: Time-varying Statistical Characteristics Of Connectedness Nementioning

confidence: 96%

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

Zhang

Chen

2020

Sustainability

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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.

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Section: Main Findingssupporting

confidence: 92%

Section: Time-varying Statistical Characteristics Of Connectedness Nementioning

confidence: 96%

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

Zhang

Chen

2020

Sustainability

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“…Liu and Tse [31] used five years of stock index data from 67 countries and use Pearson's correlation to generate a complex network. Gong et al [32] employed the transfer entropy method to analyse interactions between national stock markets and discovered that countries affected by the crisis become closer to each other and the total network connectedness rises during the crisis. Chen et al [33] used complex network theories to measure systemic risks in the stock market and developed dynamic topological indicators to analyse financial contagion and qualify the magnitude of systemic risks.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Analyses of Contagion Risk and Module Evolution on the SSE A-Shares Market Based on Minimum Information Entropy

Chen

Wang

et al. 2021

Entropy

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The interactive effect is significant in the Chinese stock market, exacerbating the abnormal market volatilities and risk contagion. Based on daily stock returns in the Shanghai Stock Exchange (SSE) A-shares, this paper divides the period between 2005 and 2018 into eight bull and bear market stages to investigate interactive patterns in the Chinese financial market. We employ the Least Absolute Shrinkage and Selection Operator (LASSO) method to construct the stock network, compare the heterogeneity of bull and bear markets, and further use the Map Equation method to analyse the evolution of modules in the SSE A-shares market. Empirical results show that (1) the connected effect is more significant in bear markets than bull markets and gives rise to abnormal volatilities in the stock market; (2) a system module can be found in the network during the first four stages, and the industry aggregation effect leads to module differentiation in the last four stages; (3) some stocks have leading effects on others throughout eight periods, and medium- and small-cap stocks with poor financial conditions are more likely to become risk sources, especially in bear markets. Our conclusions are beneficial to improving investment strategies and making regulatory policies.

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“…Many studies attempt to analyze the impacts of financial crisis 2008 on stock market networks. Gong et al (2019) observed an increase in the network connectedness of global stock markets during financial crisis of 2008. Similarly, a substantial increase in the average correlations during financial crisis time period of 2008 has been observed for US stock market (Qiu et al, 2018), Pakistan stock market , Chinese stock market (Ren and Zhou, 2014), South African stock market (Majapa and Gossel, 2016), and Korean stock market .…”

Section: Introductionmentioning

confidence: 99%

Examining Network Structures and Dynamics of World Energy Companies in Stock Markets: A Complex Network Approach

Memon

Tahir

2021

IJEEP

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The energy sector occupies a mainstay role in overall growth in the modern worldwide economy. Therefore, it is essential to examine network structures and dynamics of leading energy companies of the world through complex network methods. Because, complex network methods are significant tools of studying the static and dynamics properties of the stock market, which allows us to better comprehend the stock market. We use daily prices of 147 energy stocks belonging to 34 countries of the world from 2006-2019. In addition to the overall sample, we explore networks for two sub-periods to examine the topological evolution during global recession of 2008, and energy and European debt crisis of 2011. Our results show substantial clustering of energy companies based on their geographic position during overall sample period. However, the crisis periods lead to a break in Asian and European clusters and only one prominent cluster appears in all the periods belonging to North American energy companies. We also observe few top US and European based companies occupying important and great global influence positions in the networks. In addition, time-varying topological measures indicate contraction of networks during crisis time, and an expansion in the recovery periods. More implications are also discussed.

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Measuring the network connectedness of global stock markets

Cited by 37 publications

References 12 publications

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

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

Dynamic Analyses of Contagion Risk and Module Evolution on the SSE A-Shares Market Based on Minimum Information Entropy

Examining Network Structures and Dynamics of World Energy Companies in Stock Markets: A Complex Network Approach

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