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Dependence and risk spillovers between mainland China and London stock markets before and after the Stock Connect programs

Author

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  • Yang, Kun
  • Wei, Yu
  • He, Jianmin
  • Li, Shouwei

Abstract

This paper investigates the variations in dependence and risk spillover between mainland China and London stock markets before and after two Stock Connect programs launched in China, by measuring the Conditional Value-at-Risk (CoVaR) and Conditional Expected Shortfall (CoES) using realized volatility which is calculated with high-frequency data and Copula model. The empirical results show that, first, the Shanghai–Hong Kong Stock Connect program enhances the dependence between mainland China and London stock markets, while the overall dependence decreases slightly after the Shenzhen–Hong Kong Stock Connect program. More specifically, the lower-tail dependence between mainland China and London stock markets continues to increase after the second Stock Connect scheme. The upper-tail dependence, however, rises up after the first Stock Connect scheme but decreases sharply after the second Connect scheme. Second, the Stock Connect programs strength the downside risk spillovers between the mainland China and London markets in most cases. Finally, the equal tests on risk spillovers show significant asymmetric features, indicating that the mainland China stock markets are more likely affected by the London stock market.

Suggested Citation

  • Yang, Kun & Wei, Yu & He, Jianmin & Li, Shouwei, 2019. "Dependence and risk spillovers between mainland China and London stock markets before and after the Stock Connect programs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    • Handle: RePEc:eee:phsmap:v:526:y:2019:i:c:s0378437119305059
    DOI: 10.1016/j.physa.2019.04.119
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    Citations

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    Cited by:

    1. Cui Jinxin & Zou Huiwen, 2020. "Connectedness Among Economic Policy Uncertainties: Evidence from the Time and Frequency Domain Perspectives," Journal of Systems Science and Information, De Gruyter, vol. 8(5), pages 401-433, October.
    2. Han, Xuyuan & Liu, Zhenya & Wang, Shixuan, 2022. "An R-vine copula analysis of non-ferrous metal futures with application in Value-at-Risk forecasting," Journal of Commodity Markets, Elsevier, vol. 25(C).
    3. Andy Wui-Wing Cheng & Nikolai Sheung-Chi Chow & David Kam-Hung Chui & Wing-Keung Wong, 2019. "The Three Musketeers Relationships between Hong Kong, Shanghai and Shenzhen Before and After Shanghai–Hong Kong Stock Connect," Sustainability, MDPI, vol. 11(14), pages 1-20, July.
    4. Shi, Yujie & Wang, Liming & Ke, Jian, 2021. "Does the US-China trade war affect co-movements between US and Chinese stock markets?," Research in International Business and Finance, Elsevier, vol. 58(C).
    5. Yiting Fan & Rui Fang, 2022. "Some Results on Measures of Interaction among Risks," Mathematics, MDPI, vol. 10(19), pages 1-19, October.
    6. Yao, Can-Zhong & Li, Min-Jian, 2023. "GARCH-MIDAS-GAS-copula model for CoVaR and risk spillover in stock markets," The North American Journal of Economics and Finance, Elsevier, vol. 66(C).
    7. Yang, Kun & Wei, Yu & Li, Shouwei & He, Jianmin, 2020. "Asymmetric risk spillovers between Shanghai and Hong Kong stock markets under China’s capital account liberalization," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    8. Bai, Lan & Zhang, Xuhui & Liu, Yuntong & Wang, Qian, 2019. "Economic risk contagion among major economies: New evidence from EPU spillover analysis in time and frequency domains," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).

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