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Return and volatility spillovers between Chinese and US clean energy related stocks

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  • Karel Janda
  • Ladislav Kristoufek
  • Binyi Zhang

Abstract

This paper aims to empirically investigate the dynamic connectedness between oil prices and stock returns of clean energy-related and technology companies in China and U.S. financial markets. We apply three multivariate GARCH model specifications (CCC, DCC and ADCC) to investigate the return and volatility spillovers among price and return series. We use rolling window analysis to forecast out-of-sample one-step-ahead dynamic conditional correlations and time-varying optimal hedge ratios. Our results suggest that Invesco China Technology ETF (CQQQ) is the best asset to hedge Chinese clean energy stocks followed by WTI, ECO, and PSE. Our results are reasonably robust to the choice of different model refits and forecast length of rolling window analysis. Our empirical findings provide investors and policymakers with the systematic understanding of return and volatility connectedness between China and U.S. clean energy stock markets.

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  • Karel Janda & Ladislav Kristoufek & Binyi Zhang, 2022. "Return and volatility spillovers between Chinese and US clean energy related stocks," CAMA Working Papers 2022-17, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    • Handle: RePEc:een:camaaa:2022-17
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    3. Susovon Jana & Tarak N. Sahu, 2023. "Is the cryptocurrency market a hedge against stock market risk? A Wavelet and GARCH approach," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 52(3), November.
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    5. Ahmed, Walid M.A. & Sleem, Mohamed A.E., 2023. "Short- and long-run determinants of the price behavior of US clean energy stocks: A dynamic ARDL simulations approach," Energy Economics, Elsevier, vol. 124(C).
    6. Gong, Xiao-Li & Zhao, Min & Wu, Zhuo-Cheng & Jia, Kai-Wen & Xiong, Xiong, 2023. "Research on tail risk contagion in international energy markets—The quantile time-frequency volatility spillover perspective," Energy Economics, Elsevier, vol. 121(C).
    7. Vilija Aleknevičien&# & Asta Bendoraityt&#, 2023. "Role of Green Finance in Greening the Economy: Conceptual Approach," Central European Business Review, Prague University of Economics and Business, vol. 2023(2), pages 105-130.
    8. Qi, Haozhi & Ma, Lijun & Peng, Pin & Chen, Hao & Li, Kang, 2022. "Dynamic connectedness between clean energy stock markets and energy commodity markets during times of COVID-19: Empirical evidence from China," Resources Policy, Elsevier, vol. 79(C).
    9. Xiaohong Qi & Guofu Zhang & Yuqi Wang, 2022. "Distributional Predictability and Quantile Connectedness of New Energy, Steam Coal, and High-Tech in China," Sustainability, MDPI, vol. 14(21), pages 1-16, October.
    10. Cheikh, Nidhaleddine Ben & Zaied, Younes Ben, 2023. "Investigating the dynamics of crude oil and clean energy markets in times of geopolitical tensions," Energy Economics, Elsevier, vol. 124(C).
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    More about this item

    Keywords

    Clean energy; Hedge effectiveness; Rolling window analysis;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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