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Distributional Predictability and Quantile Connectedness of New Energy, Steam Coal, and High-Tech in China

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  • Xiaohong Qi

    (College of Economics and Management, Tianjin University of Science and Technology, Tianjin 300453, China)

  • Guofu Zhang

    (College of Economics and Management, Tianjin University of Science and Technology, Tianjin 300453, China)

  • Yuqi Wang

    (School of International Economics and Trade, Xinjiang University of Finance and Economics, Urumqi 830026, China)

Abstract

From a novel quantile perspective, this paper employs nonparametric quantile causality and quantile connectedness to investigate distributional predictability and spillover effects among new energy, steam coal, and high-tech under normal and tail conditions. We first identify the quantile causality: there is a unidirectional causality between the quantile orders 0.1 and 0.4 from technology high-tech to new energy, indicating that the stock price of technology companies has a predictive power of the stock prices of new energy companies when the latter is relatively low. Next, in terms of quantile connectedness, while the risk shocks to the system do not propagate strongly around the median, there are strong spillover effects in both tails. Moreover, high-tech and new energy contribute most of the system’s spillovers, and high-tech is the main net shock transmitter to all other variables. We further find that the strength of spillovers may depend on events such as China’s stock market rout of 2015 and the COVID-19 pandemic.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14176-:d:958233
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    as
    1. Lin, Boqiang & Chen, Yufang, 2019. "Dynamic linkages and spillover effects between CET market, coal market and stock market of new energy companies: A case of Beijing CET market in China," Energy, Elsevier, vol. 172(C), pages 1198-1210.
    2. Chatziantoniou, Ioannis & Gabauer, David & Stenfors, Alexis, 2021. "Interest rate swaps and the transmission mechanism of monetary policy: A quantile connectedness approach," Economics Letters, Elsevier, vol. 204(C).
    3. Tan, Xueping & Geng, Yong & Vivian, Andrew & Wang, Xinyu, 2021. "Measuring risk spillovers between oil and clean energy stocks: Evidence from a systematic framework," Resources Policy, Elsevier, vol. 74(C).
    4. Wang, Ying & Zhang, Dayong & Ji, Qiang & Shi, Xunpeng, 2020. "Regional renewable energy development in China: A multidimensional assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    5. Galvao Jr., Antonio F., 2009. "Unit root quantile autoregression testing using covariates," Journal of Econometrics, Elsevier, vol. 152(2), pages 165-178, October.
    6. Karel Janda & Ladislav Kristoufek & Binyi Zhang, 2021. "Return and volatility spillovers between Chinese and U.S. Clean Energy Related Stocks: Evidence from VAR-MGARCH estimations," FFA Working Papers 4.001, Prague University of Economics and Business, revised 17 Jan 2022.
    7. Fahmy, Hany, 2022. "The rise in investors’ awareness of climate risks after the Paris Agreement and the clean energy-oil-technology prices nexus," Energy Economics, Elsevier, vol. 106(C).
    8. Reboredo, Juan C. & Ugolini, Andrea, 2018. "The impact of energy prices on clean energy stock prices. A multivariate quantile dependence approach," Energy Economics, Elsevier, vol. 76(C), pages 136-152.
    9. Saeed, Tareq & Bouri, Elie & Alsulami, Hamed, 2021. "Extreme return connectedness and its determinants between clean/green and dirty energy investments," Energy Economics, Elsevier, vol. 96(C).
    10. Li, Jianglong & Xie, Chunping & Long, Houyin, 2019. "The roles of inter-fuel substitution and inter-market contagion in driving energy prices: evidences from China’s coal market," LSE Research Online Documents on Economics 102540, London School of Economics and Political Science, LSE Library.
    11. Qu, Fang & Chen, Yufeng & Zheng, Biao, 2021. "Is new energy driven by crude oil, high-tech sector or low-carbon notion? New evidence from high-frequency data," Energy, Elsevier, vol. 230(C).
    12. Managi, Shunsuke & Okimoto, Tatsuyoshi, 2013. "Does the price of oil interact with clean energy prices in the stock market?," Japan and the World Economy, Elsevier, vol. 27(C), pages 1-9.
    13. Kassouri, Yacouba & Kacou, Kacou Yves Thierry & Alola, Andrew Adewale, 2021. "Are oil-clean energy and high technology stock prices in the same straits? Bubbles speculation and time-varying perspectives," Energy, Elsevier, vol. 232(C).
    14. Zheng, Biao & Zhang, Yuquan W. & Qu, Fang & Geng, Yong & Yu, Haishan, 2022. "Do rare earths drive volatility spillover in crude oil, renewable energy, and high-technology markets? — A wavelet-based BEKK- GARCH-X approach," Energy, Elsevier, vol. 251(C).
    15. Zhang, Guofu & Du, Ziping, 2017. "Co-movements among the stock prices of new energy, high-technology and fossil fuel companies in China," Energy, Elsevier, vol. 135(C), pages 249-256.
    16. Roger Koenker & Zhijie Xiao, 2004. "Unit Root Quantile Autoregression Inference," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 775-787, January.
    17. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    18. Umar, Muhammad & Farid, Saqib & Naeem, Muhammad Abubakr, 2022. "Time-frequency connectedness among clean-energy stocks and fossil fuel markets: Comparison between financial, oil and pandemic crisis," Energy, Elsevier, vol. 240(C).
    19. Henriques, Irene & Sadorsky, Perry, 2008. "Oil prices and the stock prices of alternative energy companies," Energy Economics, Elsevier, vol. 30(3), pages 998-1010, May.
    20. Tiwari, Aviral Kumar & Nasreen, Samia & Hammoudeh, Shawkat & Selmi, Refk, 2021. "Dynamic dependence of oil, clean energy and the role of technology companies: New evidence from copulas with regime switching," Energy, Elsevier, vol. 220(C).
    21. Maghyereh, Aktham I. & Awartani, Basel & Abdoh, Hussein, 2019. "The co-movement between oil and clean energy stocks: A wavelet-based analysis of horizon associations," Energy, Elsevier, vol. 169(C), pages 895-913.
    22. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
    23. Mehmet Balcilar & Stelios Bekiros & Rangan Gupta, 2017. "The role of news-based uncertainty indices in predicting oil markets: a hybrid nonparametric quantile causality method," Empirical Economics, Springer, vol. 53(3), pages 879-889, November.
    24. Janda, Karel & Kristoufek, Ladislav & Zhang, Binyi, 2022. "Return and volatility spillovers between Chinese and U.S. clean energy related stocks," Energy Economics, Elsevier, vol. 108(C).
    25. Ji, Qiang & Zhang, Dayong, 2019. "How much does financial development contribute to renewable energy growth and upgrading of energy structure in China?," Energy Policy, Elsevier, vol. 128(C), pages 114-124.
    26. Bouri, Elie & Saeed, Tareq & Vo, Xuan Vinh & Roubaud, David, 2021. "Quantile connectedness in the cryptocurrency market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 71(C).
    27. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    28. Sadorsky, Perry, 2012. "Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies," Energy Economics, Elsevier, vol. 34(1), pages 248-255.
    29. Shahbaz, Muhammad & Trabelsi, Nader & Tiwari, Aviral Kumar & Abakah, Emmanuel Joel Aikins & Jiao, Zhilun, 2021. "Relationship between green investments, energy markets, and stock markets in the aftermath of the global financial crisis," Energy Economics, Elsevier, vol. 104(C).
    30. Batten, Jonathan A. & Brzeszczynski, Janusz & Ciner, Cetin & Lau, Marco C.K. & Lucey, Brian & Yarovaya, Larisa, 2019. "Price and volatility spillovers across the international steam coal market," Energy Economics, Elsevier, vol. 77(C), pages 119-138.
    31. Geng, Jiang-Bo & Liu, Changyu & Ji, Qiang & Zhang, Dayong, 2021. "Do oil price changes really matter for clean energy returns?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    32. Dean, Warren G. & Faff, Robert W. & Loudon, Geoffrey F., 2010. "Asymmetry in return and volatility spillover between equity and bond markets in Australia," Pacific-Basin Finance Journal, Elsevier, vol. 18(3), pages 272-289, June.
    33. Li, Jianglong & Xie, Chunping & Long, Houyin, 2019. "The roles of inter-fuel substitution and inter-market contagion in driving energy prices: Evidences from China’s coal market," Energy Economics, Elsevier, vol. 84(C).
    34. Wen, Xiaoqian & Guo, Yanfeng & Wei, Yu & Huang, Dengshi, 2014. "How do the stock prices of new energy and fossil fuel companies correlate? Evidence from China," Energy Economics, Elsevier, vol. 41(C), pages 63-75.
    35. Jeong, Kiho & Härdle, Wolfgang K. & Song, Song, 2012. "A Consistent Nonparametric Test For Causality In Quantile," Econometric Theory, Cambridge University Press, vol. 28(4), pages 861-887, August.
    36. Bondia, Ripsy & Ghosh, Sajal & Kanjilal, Kakali, 2016. "International crude oil prices and the stock prices of clean energy and technology companies: Evidence from non-linear cointegration tests with unknown structural breaks," Energy, Elsevier, vol. 101(C), pages 558-565.
    37. Nishiyama, Yoshihiko & Hitomi, Kohtaro & Kawasaki, Yoshinori & Jeong, Kiho, 2011. "A consistent nonparametric test for nonlinear causality—Specification in time series regression," Journal of Econometrics, Elsevier, vol. 165(1), pages 112-127.
    38. Sun, Chuanwang & Ding, Dan & Fang, Xingming & Zhang, Huiming & Li, Jianglong, 2019. "How do fossil energy prices affect the stock prices of new energy companies? Evidence from Divisia energy price index in China's market," Energy, Elsevier, vol. 169(C), pages 637-645.
    39. Hammoudeh, Shawkat & Mokni, Khaled & Ben-Salha, Ousama & Ajmi, Ahdi Noomen, 2021. "Distributional predictability between oil prices and renewable energy stocks: Is there a role for the COVID-19 pandemic?," Energy Economics, Elsevier, vol. 103(C).
    40. Yahya, Muhammad & Kanjilal, Kakali & Dutta, Anupam & Uddin, Gazi Salah & Ghosh, Sajal, 2021. "Can clean energy stock price rule oil price? New evidences from a regime-switching model at first and second moments," Energy Economics, Elsevier, vol. 95(C).
    41. Chen, Jinyu & Liang, Zhipeng & Ding, Qian & Liu, Zhenhua, 2022. "Extreme spillovers among fossil energy, clean energy, and metals markets: Evidence from a quantile-based analysis," Energy Economics, Elsevier, vol. 107(C).
    42. Zhang, Hao & Cai, Guixin & Yang, Dongxiao, 2020. "The impact of oil price shocks on clean energy stocks: Fresh evidence from multi-scale perspective," Energy, Elsevier, vol. 196(C).
    43. Gu, Fu & Wang, Jiqiang & Guo, Jianfeng & Fan, Ying, 2020. "How the supply and demand of steam coal affect the investment in clean energy industry? Evidence from China," Resources Policy, Elsevier, vol. 69(C).
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