Measuring systemic risk contribution of global stock markets: A dynamic tail risk network approach

Wang, Ze; Gao, Xiangyun; Huang, Shupei; Sun, Qingru; Chen, Zhihua; Tang, Renwu; Di, Zengru

doi:10.1016/j.irfa.2022.102361

Cited by 20 publications

(8 citation statements)

References 92 publications

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“…Most of these studies suggest that there is a short‐term negative impact. When the financial market is impacted by some adverse events, the risks induced by these events are continuously transmitted among the stock markets, leading to a ‘contagion effect’ (Wang et al, 2022). Under this circumstance, the investors will update their expectations about firms' long‐term growth, reacting immediately by a selling of stocks (Jeong & Yun Yoo, 2011).…”

Section: Resultsmentioning

confidence: 99%

Sudden crisis events and firms' stock price fluctuations: A multidimensional heterogeneity perspective

Tian,

Wang,

Xie

et al. 2024

Contingencies & Crisis Mgmt

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This study takes listed companies that have experienced sudden crisis events as samples. The event study method and multiple regression analysis are further adopted to explore the relationship between sudden crisis events and the stock price fluctuations of listed companies from the perspective of multidimensional characteristics. Findings reveal that sudden crisis events have a significant impact on stock price fluctuations. However, the firms' response frequency and response time to crisis events can exert a buffering role in their adverse association. Further analysis shows that the stock price fluctuations also vary with different heterogeneous characteristics including crisis event types, ownership types, firm competitiveness and firm size. The multidimensional analysis targeting the impact of sudden crisis events on the stock market is conducive to helping firms grasp the rhythm of crisis events and further implement some practical and feasible crisis management measures.

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Section: Resultsmentioning

confidence: 99%

Sudden crisis events and firms' stock price fluctuations: A multidimensional heterogeneity perspective

Tian,

Wang,

Xie

et al. 2024

Contingencies & Crisis Mgmt

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“…Abuzayed et al (2021) combine CoVaR and ∆CoVaR calculated by the DCC-GARCH model to analyze the systemic risk spillovers between the international equity market and domestic equity markets in the countries that are most impacted by the COVID-19 pandemic. Duarte and Eisenbach (2021) and Wang et al (2022) employ ∆CoVaR to investigate the systemic risk and risk contagion of the U.S. banking industry and global equity markets, respectively. Aloui et al (2022) calculate CoVaR to explore the risk spillover effects from the Chinese equity market to the G7 equity markets before and during the COVID-19 pandemic.…”

Section: Literature Reviewmentioning

confidence: 99%

Tail dependence structure and extreme risk spillover effects between the international agricultural futures and spot markets

Dai¹,

Dai²,

Zhou³

2023

Preprint

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This paper combines the Copula-CoVaR approach with the ARMA-GARCH-skewed Student-t model to investigate the tail dependence structure and extreme risk spillover effects between the international agricultural futures and spot markets, taking four main agricultural commodities, namely soybean, maize, wheat, and rice as examples. The empirical results indicate that the tail dependence structures for the four futures-spot pairs are quite different, and each of them exhibits a certain degree of asymmetry. In addition, the futures market for each agricultural commodity has significant and robust extreme downside and upside risk spillover effects on the spot market, and the downside risk spillover effects for both soybeans and maize are significantly stronger than their corresponding upside risk spillover effects, while there is no significant strength difference between the two risk spillover effects for wheat, and rice. This study provides a theoretical basis for strengthening global food cooperation and maintaining global food security, and has practical significance for investors to use agricultural commodities for risk management and portfolio optimization.

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“…The research by Wang et al [31] addresses the measurement of the systemic risk contribution (SRC) of country-level stock markets to understand the rise of extreme risks worldwide to prevent potential financial crises. The proposed measure of SRC is based on quantifying tail risk propagation's domino effect using CoVaR and the cascading failure network model.…”

Section: Literature Reviewmentioning

confidence: 99%

The Systemic Risk Approach Based on Implied and Realized Volatility

2023

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Measuring systemic risk contribution of global stock markets: A dynamic tail risk network approach

Cited by 20 publications

References 92 publications

Sudden crisis events and firms' stock price fluctuations: A multidimensional heterogeneity perspective

Sudden crisis events and firms' stock price fluctuations: A multidimensional heterogeneity perspective

Tail dependence structure and extreme risk spillover effects between the international agricultural futures and spot markets

The Systemic Risk Approach Based on Implied and Realized Volatility

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