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The dependence structure in volatility between Shanghai and Shenzhen stock market in China

Author

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  • Mingyuan Guo
  • Xu Wang

Abstract

Purpose - – The purpose of this paper is to analyse the dependence structure in volatility between Shanghai and Shenzhen stock market in China based on high-frequency data. Design/methodology/approach - – Using a multiplicative error model (hereinafter MEM) to describe the margins in volatility of China’s Shanghai and Shenzhen stock market, this study adopts static and time-varying copulas, respectively, estimated by maximum likelihood estimation method to describe the dependence structure in volatility between Shanghai and Shenzhen stock market in China. Findings - – This paper has identified the asymmetrical dependence structure in financial market volatility more precisely. Gumbel copula could best fit the empirical distribution as it can capture the relatively high dependence degree in the upper tail part corresponding to the period of volatile price fluctuation in both static and dynamic view. Originality/value - – Previous scholars mostly use GARCH model to describe the margins for price volatility. As MEM can efficiently characterize the volatility estimators, this paper uses MEM to model the margins for the market volatility directly based on high-frequency data, and proposes a proper distribution for the innovation in the marginal models. Then we could use copula-MEM other than copula-GARCH model to study on the dependence structure in volatility between Shanghai and Shenzhen stock market in China from a microstructural perspective.

Suggested Citation

  • Mingyuan Guo & Xu Wang, 2016. "The dependence structure in volatility between Shanghai and Shenzhen stock market in China," China Finance Review International, Emerald Group Publishing Limited, vol. 6(3), pages 264-283, August.
    • Handle: RePEc:eme:cfripp:v:6:y:2016:i:3:p:264-283
    DOI: 10.1108/CFRI-09-2015-0122
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    Citations

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    Cited by:

    1. Chen, Lin & Wen, Fenghua & Li, Wanyang & Yin, Hua & Zhao, Lili, 2022. "Extreme risk spillover of the oil, exchange rate to Chinese stock market: Evidence from implied volatility indexes," Energy Economics, Elsevier, vol. 107(C).
    2. Qunwei Wang & Xingyu Dai & Dequn Zhou, 2020. "Dynamic Correlation and Risk Contagion Between “Black” Futures in China: A Multi-scale Variational Mode Decomposition Approach," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1117-1150, April.
    3. Cui, Yan & Feng, Yun, 2020. "Composite hedge and utility maximization for optimal futures hedging," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 15-32.
    4. Ji, Qiang & Liu, Bing-Yue & Nehler, Henrik & Uddin, Gazi Salah, 2018. "Uncertainties and extreme risk spillover in the energy markets: A time-varying copula-based CoVaR approach," Energy Economics, Elsevier, vol. 76(C), pages 115-126.

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