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An analysis of the clustering effect of a jump risk complex network in the Chinese stock market

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  • Hu, Sunyang
  • Gu, Zongyuan
  • Wang, Yifeng
  • Zhang, Xiaolei

Abstract

With the development of Chinese financial market, the correlation between stock price jump risks cannot be ignored. This study uses the complex network method to analyze the clustering effect of stock price jumps. Taking a sample of stocks from the CSI 300 Index, the realized jumps are extracted from the 5-minute high frequency data using the MinRV method. The authors use the Minimum Spanning Tree algorithm to construct a complex network of stock price jumps. It is found that there is a clear correlation among stocks in the entire jump network. The jump in manufacturing industry stocks plays the most important role in the network. The Modular Q function and the Fast Unfolding algorithm are used to divide the entire complex network and study the differences in jump correlation between different communities. The result shows that the correlation among the financial industry stocks is stronger, and a large fluctuation in the price of one financial stock can cause the price of another financial stock to fluctuate significantly.

Suggested Citation

  • Hu, Sunyang & Gu, Zongyuan & Wang, Yifeng & Zhang, Xiaolei, 2019. "An analysis of the clustering effect of a jump risk complex network in the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 622-630.
    • Handle: RePEc:eee:phsmap:v:523:y:2019:i:c:p:622-630
    DOI: 10.1016/j.physa.2019.01.114
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    References listed on IDEAS

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    1. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    2. Ole E. Barndorff-Nielsen & Neil Shephard, 2004. "Econometric Analysis of Realized Covariation: High Frequency Based Covariance, Regression, and Correlation in Financial Economics," Econometrica, Econometric Society, vol. 72(3), pages 885-925, May.
    3. Tse, Chi K. & Liu, Jing & Lau, Francis C.M., 2010. "A network perspective of the stock market," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 659-667, September.
    4. Andersen, Torben G. & Bollerslev, Tim & Huang, Xin, 2011. "A reduced form framework for modeling volatility of speculative prices based on realized variation measures," Journal of Econometrics, Elsevier, vol. 160(1), pages 176-189, January.
    5. repec:bla:jfinan:v:53:y:1998:i:1:p:219-265 is not listed on IDEAS
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    Cited by:

    1. Huang, Chuangxia & Zhao, Xian & Deng, Yunke & Yang, Xiaoguang & Yang, Xin, 2022. "Evaluating influential nodes for the Chinese energy stocks based on jump volatility spillover network," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 81-94.
    2. Bouri, Elie & Lucey, Brian & Saeed, Tareq & Vo, Xuan Vinh, 2021. "The realized volatility of commodity futures: Interconnectedness and determinants#," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 139-151.
    3. Kaihao Liang & Shuliang Li & Wenfeng Zhang & Chaolong Zhang, 2024. "Research on Stock Market Risk Contagion of Major Debt Crises Based on Complex Network Models—The Case of Evergrande in China," Mathematics, MDPI, vol. 12(11), pages 1-13, May.
    4. Chuangxia Huang & Xian Zhao & Renli Su & Xiaoguang Yang & Xin Yang, 2022. "Dynamic network topology and market performance: A case of the Chinese stock market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1962-1978, April.

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