Extreme quantile spillovers and drivers among clean energy,electricity and energy metals markets |
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| Institution: | 1. School of Mathematics and Statistics, Central South University, Changsha 410083, China;2. Institute of Metal Resources Strategy, Central South University, Changsha 410083, China;3. School of Finance, Hebei University of Economics and Business, Shijiazhuang 050062, China;4. School of Business, Central South University, Changsha 410083, China;1. College of Management and Economics, Tianjin University, Tianjin 300072, PR China;2. DCU Business School, Dublin City University, Glasnevin, Dublin 9, Ireland;3. School of Finance, Nankai University, Tianjin 300350, PR China;1. Hull University Business School, University of Hull, Hull HU67RX, United Kingdom;2. China Institute for Actuarial Science, Central University of Finance and Economics, Beijing 100081, China;1. Department of Finance, School of Business, Hohai University, China;2. Department of Management Science and Engineering, School of Business, Hohai University, China;1. Belk College of Business, University of North Carolina at Charlotte, 9209 Mary Alexander Rd, Charlotte, NC 28262, USA;2. Zanvyl Krieger School of Arts and Sciences Johns Hopkins University, 1717 Massachusetts Avenue NW, Suite 104S, Washington, DC 20036, USA;3. Department of Business & Economics, 1600 Burrstone RD, Utica University, Utica, NY 13502, USA |
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| Abstract: | This paper examines return and volatility spillover effects among the clean energy (electric vehicles, solar and wind), electricity and 8 energy metals (silver, tin, nickel, cobalt, lead, zinc, aluminum and copper) markets and their drivers under the conditions of the mean and extreme quantiles. The results show moderate spillovers among the clean energy, electricity and energy metals markets, and greater connectivity among the three markets under extreme quantile conditions. Among them, the clean energy markets always play the role of the transmitter, and the electricity market always plays the role of the receiver of spillover effects. In addition, the return and volatility spillovers among the three markets have remarkable time-varying features, and they increase dramatically when extreme events occur, especially under extreme quantile conditions. Finally, we reveal the drivers of return and volatility spillovers among these markets by the OLS and quantile regression methods. The COVID-19 and the Arca Tech 100 (PSE) index are found to be important drivers. |
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