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Multiscale extreme risk spillovers among the Chinese mainland, Hong Kong, and London stock markets: Comparing the impacts of three Stock Connect programs

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  • Yao, Yinhong
  • Li, Jingyu
  • Chen, Wei

Abstract

This paper investigates the multiscale extreme risk spillovers among Shanghai, Shenzhen, Hong Kong, and London stock markets by combining the wavelet decomposition and time-varying copula-CoVaR methods, and compares the impacts of three Stock Connect programs proposed by China. Based on the daily closing prices of four stock indexes ranging from Jan 4, 2013, to Jan 21, 2022, we find that multiscale extreme risk spillovers significantly exist among four stock markets with dynamic and asymmetric characteristics. The magnitudes of risk spillover effects decrease as the time scale increases. The implementation of the SH-HK and SH-L programs could improve the spillovers between related stock markets, while the SZ-HK program possesses a slightly decreasing effect, and their impacts are lower than the external shock events. These results systematically reveal the extreme risk transmission features among four stock markets, which are beneficial for practical portfolio optimization and risk management in the opening stock markets.

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  • Yao, Yinhong & Li, Jingyu & Chen, Wei, 2024. "Multiscale extreme risk spillovers among the Chinese mainland, Hong Kong, and London stock markets: Comparing the impacts of three Stock Connect programs," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 1217-1233.
    • Handle: RePEc:eee:reveco:v:89:y:2024:i:pa:p:1217-1233
    DOI: 10.1016/j.iref.2023.08.020
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    as
    1. Wang, Qiyu & Chong, Terence Tai-Leung, 2018. "Co-integrated or not? After the Shanghai–Hong Kong and Shenzhen–Hong Kong Stock Connection Schemes," Economics Letters, Elsevier, vol. 163(C), pages 167-171.
    2. Huo, Rui & Ahmed, Abdullahi D., 2017. "Return and volatility spillovers effects: Evaluating the impact of Shanghai-Hong Kong Stock Connect," Economic Modelling, Elsevier, vol. 61(C), pages 260-272.
    3. Ma, Rufei & Deng, Chengtao & Cai, Huan & Zhai, Pengxiang, 2019. "Does Shanghai-Hong Kong Stock Connect drive market comovement between Shanghai and Hong Kong: A new evidence," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    4. 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).
    5. Naeem, Muhammad & Umar, Zaghum & Ahmed, Sheraz & Ferrouhi, El Mehdi, 2020. "Dynamic dependence between ETFs and crude oil prices by using EGARCH-Copula approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    6. Yonghong Jiang & Jinqi Mu & He Nie & Lanxin Wu, 2022. "Time‐frequency analysis of risk spillovers from oil to BRICS stock markets: A long‐memory Copula‐CoVaR‐MODWT method," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3386-3404, July.
    7. Chen, Yajie & Guo, Kun & Ji, Qiang & Zhang, Dayong, 2023. "“Not all climate risks are alike”: Heterogeneous responses of financial firms to natural disasters in China," Finance Research Letters, Elsevier, vol. 52(C).
    8. Yang, Lu & Yang, Lei & Hamori, Shigeyuki, 2018. "Determinants of dependence structures of sovereign credit default swap spreads between G7 and BRICS countries," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 19-34.
    9. Xu, Qifa & Jin, Bei & Jiang, Cuixia, 2021. "Measuring systemic risk of the Chinese banking industry: A wavelet-based quantile regression approach," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    10. Wu, Ming & Ohk, Ki Yool, 2023. "Who benefits more? Shanghai-Hong Kong stock Connect—“Through Train”," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 409-427.
    11. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    12. Hanif, Waqas & Mensi, Walid & Vo, Xuan Vinh, 2021. "Impacts of COVID-19 outbreak on the spillovers between US and Chinese stock sectors," Finance Research Letters, Elsevier, vol. 40(C).
    13. Liu, Hung-Chun & Chiang, Shu-Mei & Cheng, Nick Ying-Pin, 2012. "Forecasting the volatility of S&P depositary receipts using GARCH-type models under intraday range-based and return-based proxy measures," International Review of Economics & Finance, Elsevier, vol. 22(1), pages 78-91.
    14. Aloui, Riadh & Ben Jabeur, Sami & Mefteh-Wali, Salma, 2022. "Tail-risk spillovers from China to G7 stock market returns during the COVID-19 outbreak: A market and sectoral analysis," Research in International Business and Finance, Elsevier, vol. 62(C).
    15. Sun, Xiaolei & Liu, Chang & Wang, Jun & Li, Jianping, 2020. "Assessing the extreme risk spillovers of international commodities on maritime markets: A GARCH-Copula-CoVaR approach," International Review of Financial Analysis, Elsevier, vol. 68(C).
    16. Ding, Hao & Ji, Qiang & Ma, Rufei & Zhai, Pengxiang, 2022. "High-carbon screening out: A DCC-MIDAS-climate policy risk method," Finance Research Letters, Elsevier, vol. 47(PA).
    17. Burdekin, Richard C.K. & Siklos, Pierre L., 2018. "Quantifying the impact of the November 2014 Shanghai-Hong Kong Stock Connect," International Review of Economics & Finance, Elsevier, vol. 57(C), pages 156-163.
    18. Mariani, Maria C. & Bhuiyan, Md Al Masum & Tweneboah, Osei K. & Beccar-Varela, Maria P. & Florescu, Ionut, 2020. "Analysis of stock market data by using Dynamic Fourier and Wavelets techniques," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    19. Garg, Jyoti & Karmakar, Madhusudan & Paul, Samit, 2023. "A study on equity home bias using vine copula approach," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    20. Dai, Xingyu & Wang, Qunwei & Zha, Donglan & Zhou, Dequn, 2020. "Multi-scale dependence structure and risk contagion between oil, gold, and US exchange rate: A wavelet-based vine-copula approach," Energy Economics, Elsevier, vol. 88(C).
    21. Gençay, Ramazan & Signori, Daniele, 2015. "Multi-scale tests for serial correlation," Journal of Econometrics, Elsevier, vol. 184(1), pages 62-80.
    22. 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.
    23. Luo, Jiawen & Marfatia, Hardik A. & Ji, Qiang & Klein, Tony, 2023. "Co-volatility and asymmetric transmission of risks between the global oil and China's futures markets," Energy Economics, Elsevier, vol. 117(C).
    24. Bai, Xiwen, 2021. "Tanker freight rates and economic policy uncertainty: A wavelet-based copula approach," Energy, Elsevier, vol. 235(C).
    25. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    26. Luo, Changqing & Liu, Lan & Wang, Da, 2021. "Multiscale financial risk contagion between international stock markets: Evidence from EMD-Copula-CoVaR analysis," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    27. Fan, Qingliang & Wang, Ting, 2017. "The impact of Shanghai–Hong Kong Stock Connect policy on A-H share price premium," Finance Research Letters, Elsevier, vol. 21(C), pages 222-227.
    28. Liu, Clark & Wang, Shujing & Wei, K.C. John, 2021. "Demand shock, speculative beta, and asset prices: Evidence from the Shanghai-Hong Kong Stock Connect program," Journal of Banking & Finance, Elsevier, vol. 126(C).
    29. 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).
    30. Chen, Zhimin & Ibragimov, Rustam, 2019. "One country, two systems? The heavy-tailedness of Chinese A- and H- share markets," Emerging Markets Review, Elsevier, vol. 38(C), pages 115-141.
    31. Wu, Fei & Xiao, Xuanqi & Zhou, Xinyu & Zhang, Dayong & Ji, Qiang, 2022. "Complex risk contagions among large international energy firms: A multi-layer network analysis," Energy Economics, Elsevier, vol. 114(C).
    32. Shuangqi Li & Qi‐an Chen, 2021. "Do the Shanghai–Hong Kong & Shenzhen–Hong Kong Stock Connect programs enhance co‐movement between the Mainland Chinese, Hong Kong, and U.S. stock markets?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2871-2890, April.
    33. Andrew J. Patton, 2006. "Modelling Asymmetric Exchange Rate Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 527-556, May.
    34. Yahya, Muhammad & Oglend, Atle & Dahl, Roy Endré, 2019. "Temporal and spectral dependence between crude oil and agricultural commodities: A wavelet-based copula approach," Energy Economics, Elsevier, vol. 80(C), pages 277-296.
    35. Yao, Yinhong & Li, Jianping & Sun, Xiaolei, 2021. "Measuring the risk of Chinese Fintech industry: evidence from the stock index," Finance Research Letters, Elsevier, vol. 39(C).
    36. Wei Chen & Rui Li & Yinhong Yao, 2022. "Return and Volatility Spillovers among Sector Indexes in Shanghai-Shenzhen-Hong Kong Stock Markets: Evidence from the Time and Frequency Domains," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 58(13), pages 3840-3852, October.
    37. Qiang Ji & Dayong Zhang & Yuqian Zhao, 2022. "Intra-day co-movements of crude oil futures: China and the international benchmarks," Annals of Operations Research, Springer, vol. 313(1), pages 77-103, June.
    38. Wu, Weiou & Lau, Marco Chi Keung & Vigne, Samuel A., 2017. "Modelling asymmetric conditional dependence between Shanghai and Hong Kong stock markets," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1137-1149.
    39. Warshaw, Evan, 2019. "Extreme dependence and risk spillovers across north american equity markets," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 237-251.
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