The structure of the South African stock market network during COVID-19 hard lockdown

Mbatha, Vusisizwe Moses; Alovokpinhou, Sedjro Aaron

doi:10.1016/j.physa.2021.126770

Cited by 8 publications

(3 citation statements)

References 36 publications

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“…Consequently, these events can disrupt pre-existing network structures [26]. Therefore, it is crucial for clean energy investors and policymakers to comprehend the risk-return profile of clean energy investments, as well as the potential time-varying characteristics and risk aversion properties [27]. Under extreme market conditions, green bonds led to short-term growth in the clean energy sector and exerted an increasingly positive influence following the outbreak of the COVID-19 pandemic [28].…”

Section: Literature Reviewmentioning

confidence: 99%

Risk Spillovers and Network Connectedness between Clean Energy Stocks, Green Bonds, and Other Financial Assets: Evidence from China

Chen,

Fang,

Chen

et al. 2023

Energies

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As climate change impacts energy consumption, investments in clean energy are now associated with increased levels of risk and uncertainty. Consequently, the management of risk for clean energy investors has garnered significant academic attention. This study was designed to explore the risk transfers among clean energy markets, how they respond to market volatility, and how exceptional events impact the risk spillover. This was performed by examining the risk spillover of and asymmetric connectedness between clean energy markets, green bonds, and other financial markets in China, in line with the connectedness framework and minimum spanning tree technique. The findings revealed that clean energy markets exhibit heterogeneity in terms of the direction and magnitude of net risk spillover, the types of hedging assets involved, and their response to market volatility. Exceptional events, such as the Russian–Ukrainian conflict and COVID-19 pandemic, have an impact on the spillover relationships. During stable market conditions, green bonds experience fewer spillovers from clean energy markets, whereas, in times of volatility, gold markets are subjected to fewer spillovers. In the time domain, the overall long-term spillover is stronger compared to the short and medium terms. In the frequency domain, there is a significant risk of low-frequency transmission. These findings hold practical implications for energy investors in portfolio construction and for policymakers in pursuing sustainability objectives.

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Section: Literature Reviewmentioning

confidence: 99%

Risk Spillovers and Network Connectedness between Clean Energy Stocks, Green Bonds, and Other Financial Assets: Evidence from China

Chen,

Fang,

Chen

et al. 2023

Energies

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“…For example, Pereira et al applied centrality measures of weighted degree and PageRank to the DCCA network of 20 regional stock markets and concluded that European markets play a central role in the world's financial markets [28]. Mbatha and Alovokpinhou constructed the network of 134 companies from the South African stock market and found that the financial industry plays the most prominent role [29].…”

Section: Introductionmentioning

confidence: 99%

“…When the VAR-based methods characterize the directional relationship that a shock in one time series leads to the volatility change in another time series within a given lag time, the DCCA approach describes the bilateral relationship of co-fluctuation of two time series. In spite of the widespread applications of the DCCA approach in investigating the dynamics of financial networks [8,[27][28][29][30][31][32], whether, or to what extent, can such an approach represent the volatility spillover effect as indicated by the VAR-based measures is still unclear. The exploration of such a research question is crucial to deepen the understanding of the dynamics of complex financial systems, as well as enrich the application of the DCCA approach.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the connectedness of commodity future markets via the cross-correlation network

Hou

Pan²

2022

Front. Phys.

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Financial markets are widely believed to be complex systems where interdependencies exist among individual entities in the system enabling the risk spillover effect. The detrended cross-correlation analysis (DCCA) has found wide applications in examining the comovement of fluctuations among financial time series. However, to what extent can such cross-correlation represent the spillover effect is still unknown. This article constructs the DCCA network of commodity future markets and explores its proximity to the volatility spillover network. Results show a moderate agreement between the two networks. Centrality measures applied to the DCCA networks are able to identify key commodity futures that are transmitting or receiving risk spillovers. The evolution of the DCCA network reveals a significant change in the network structure during the COVID-19 pandemic in comparison to that of the pre- and post-pandemic periods. The pandemic made the commodity future markets more interconnected leading to a shorter diameter for the network. The intensified connections happen mostly between commodities from different categories. Accordingly, cross-category risk spillovers are more likely to happen during the pandemic. The analysis enriches the applications of the DCCA approach and provides useful insights into understanding the risk dynamics in commodity future markets.

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Unsupervised learning modeling of the impact of Black Swan events on financial network reconfiguration: New insights from the COVID-19 outbreak and the Russia-Ukraine war

Siudak,

Świetlik

2025

Physica A: Statistical Mechanics and its Applications

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The structure of the South African stock market network during COVID-19 hard lockdown

Cited by 8 publications

References 36 publications

Risk Spillovers and Network Connectedness between Clean Energy Stocks, Green Bonds, and Other Financial Assets: Evidence from China

Risk Spillovers and Network Connectedness between Clean Energy Stocks, Green Bonds, and Other Financial Assets: Evidence from China

Evaluating the connectedness of commodity future markets via the cross-correlation network

Unsupervised learning modeling of the impact of Black Swan events on financial network reconfiguration: New insights from the COVID-19 outbreak and the Russia-Ukraine war

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