Sudden shock and stock market network structure characteristics: A comparison of past crisis events

Chengying He, Zhang Wen, Ke Huang and Xiaoqin Ji

Technological Forecasting and Social Change, 2022, vol. 180, issue C

Abstract: Studying the correlation structure of the stock market is crucial for systemic risk and portfolio optimization. We construct the price volatility network of the Shanghai and Shenzhen 300 index components in China's stock market in the period 2007–2020. We select three representative major emergencies: the global financial crisis in 2008, the stock disaster in 2015, and the COVID-19 epidemic in 2020. First, we find that when the stock market is impacted by the major events, the network shows a cluster phenomenon. The cluster effect of the financial crisis events is smallest, while that of the epidemic events occurs most rapidly. Second, the key nodes in the stock market network have greater risk transmission ability. The manufacturing plays a crucial role during the later stages of events, while the financial industry plays an important role during the epidemic's recovery period. Third, the network structure of the stock market has an indicator effect on the systemic risk contributions. Generally, the greater a stock's eigenvector centrality, the greater its systemic risk contribution, while its closeness centrality and clustering coefficient have opposite effects. The study has important enlightenment significance for market regulators to prevent risk diffusion and reduce portfolio risk for market participants.

Keywords: Sudden shock; Stock market network; Topology structure; Systemic risk contribution (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:180:y:2022:i:c:s004016252200258x

DOI: 10.1016/j.techfore.2022.121732

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