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How do fossil energy prices affect the stock prices of new energy companies? Evidence from Divisia energy price index in China's market

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  • Sun, Chuanwang
  • Ding, Dan
  • Fang, Xingming
  • Zhang, Huiming
  • Li, Jianglong

Abstract

With the shale gas revolution and the maturity of new energy technologies, the global oil-based energy pattern began to be remodeled worldwide. From the perspective of China, coal has played a dominant leading role in the energy structure. Therefore, it is becoming increasingly irrational to replace fossil fuels with oil. This paper considers the impact of fluctuations of three fossil energy (oil, coal and natural gas) prices on new energy companies stock prices to meet the needs of policy makers and investors in this rapidly developing field. Due to the incomplete substitution among fossil fuels, this paper uses the Divisia price synthesis method to synthesize these three prices into a composite price index. Furthermore, we use a variable vector autoregressive model to explore dynamic relationships among stock prices of new energy companies and technology companies, fossil energy prices and carbon futures prices. The results reveal that previous stock prices of new energy companies had the most significant impact on the current level. However, fossil energy prices account for only a small part of stock price fluctuations of new energy companies.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:169:y:2019:i:c:p:637-645
    DOI: 10.1016/j.energy.2018.12.032
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    1. Runkle, David E, 1987. "Vector Autoregressions and Reality," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(4), pages 437-442, October.
    2. 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.
    3. Li, Junchen & Dong, Xiucheng & Shangguan, Jianxin & Hook, Mikael, 2011. "Forecasting the growth of China’s natural gas consumption," Energy, Elsevier, vol. 36(3), pages 1380-1385.
    4. 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.
    5. David E. Runkle, 1987. "Vector autoregressions and reality," Staff Report 107, Federal Reserve Bank of Minneapolis.
    6. Yang, Qing & Zhang, Lei & Wang, Xin, 2017. "Dynamic analysis on market structure of China's coal industry," Energy Policy, Elsevier, vol. 106(C), pages 498-504.
    7. 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.
    8. Bilgili, Faik & Koçak, Emrah & Bulut, Ümit & Sualp, M. Nedim, 2016. "How did the US economy react to shale gas production revolution? An advanced time series approach," Energy, Elsevier, vol. 116(P1), pages 963-977.
    9. 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.
    10. Kumar, Surender & Managi, Shunsuke & Matsuda, Akimi, 2012. "Stock prices of clean energy firms, oil and carbon markets: A vector autoregressive analysis," Energy Economics, Elsevier, vol. 34(1), pages 215-226.
    11. 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.
    12. Middleton, Richard S. & Gupta, Rajan & Hyman, Jeffrey D. & Viswanathan, Hari S., 2017. "The shale gas revolution: Barriers, sustainability, and emerging opportunities," Applied Energy, Elsevier, vol. 199(C), pages 88-95.
    13. Maximilian Auffhammer & Yazhen Gong, 2015. "China's Carbon Emissions from Fossil Fuels and Market-Based Opportunities for Control," Annual Review of Resource Economics, Annual Reviews, vol. 7(1), pages 11-34, October.
    14. Yu Liu & Hongwei Xiao & Precious Zikhali & Yingkang Lv, 2014. "Carbon Emissions in China: A Spatial Econometric Analysis at the Regional Level," Sustainability, MDPI, vol. 6(9), pages 1-19, September.
    15. Reboredo, Juan C. & Rivera-Castro, Miguel A. & Ugolini, Andrea, 2017. "Wavelet-based test of co-movement and causality between oil and renewable energy stock prices," Energy Economics, Elsevier, vol. 61(C), pages 241-252.
    16. Reboredo, Juan C., 2015. "Is there dependence and systemic risk between oil and renewable energy stock prices?," Energy Economics, Elsevier, vol. 48(C), pages 32-45.
    17. Kuck, Konstantin & Schweikert, Karsten, 2017. "A Markov regime-switching model of crude oil market integration," Journal of Commodity Markets, Elsevier, vol. 6(C), pages 16-31.
    18. Hulten, Charles R, 1973. "Divisia Index Numbers," Econometrica, Econometric Society, vol. 41(6), pages 1017-1025, November.
    19. Oberndorfer, Ulrich, 2009. "Energy prices, volatility, and the stock market: Evidence from the Eurozone," Energy Policy, Elsevier, vol. 37(12), pages 5787-5795, December.
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