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Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies

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  • Sadorsky, Perry
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    Abstract

    In this paper, multivariate GARCH models are used to model conditional correlations and to analyze the volatility spillovers between oil prices and the stock prices of clean energy companies and technology companies. Four different multivariate GARCH models (BEKK, diagonal, constant conditional correlation, and dynamic conditional correlation) are compared and contrasted. The dynamic conditional correlation model is found to fit the data the best and generates results showing that the stock prices of clean energy companies correlate more highly with technology stock prices than with oil prices. On average, a $1 long position in clean energy companies can be hedged for 20cents with a short position in the crude oil futures market.

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    Bibliographic Info

    Article provided by Elsevier in its journal Energy Economics.

    Volume (Year): 34 (2012)
    Issue (Month): 1 ()
    Pages: 248-255

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    Handle: RePEc:eee:eneeco:v:34:y:2012:i:1:p:248-255

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    Web page: http://www.elsevier.com/locate/eneco

    Related research

    Keywords: Renewable energy; Multivariate GARCH; Volatility; Oil prices;

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    Citations

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    Cited by:
    1. Ratti, Ronald A. & Vespignani, Joaquin L., 2012. "Liquidity and Crude Oil Prices: China’s Influence Over 1996-2011," MPRA Paper 48900, University Library of Munich, Germany.
    2. Yen-Hsien Lee, 2014. "An international analysis of REITs and stock portfolio management based on dynamic conditional correlation models," Financial Markets and Portfolio Management, Springer, vol. 28(2), pages 165-180, May.
    3. Mensi, Walid & Beljid, Makram & Boubaker, Adel & Managi, Shunsuke, 2013. "Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold," MPRA Paper 44395, University Library of Munich, Germany.
    4. 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.
    5. Olga Efimova & Apostolos Serletis, 2014. "Energy Markets Volatility Modelling using GARCH," Working Papers 2014-39, Department of Economics, University of Calgary, revised 24 Feb 2014.
    6. Ratti, Ronald A. & Hasan, M. Zahid, 2013. "Oil Price Shocks and Volatility in Australian Stock Returns ‎," MPRA Paper 49043, University Library of Munich, Germany.
    7. Jian Chai & Shubin Wang & Shouyang Wang & Ju’e Guo, 2012. "Demand Forecast of Petroleum Product Consumption in the Chinese Transportation Industry," Energies, MDPI, Open Access Journal, vol. 5(3), pages 577-598, March.
    8. Broadstock, David C. & Cao, Hong & Zhang, Dayong, 2012. "Oil shocks and their impact on energy related stocks in China," Energy Economics, Elsevier, vol. 34(6), pages 1888-1895.
    9. Baldi, Lucia & Peri, Massimo & Vandone, Daniela, 2014. "Clean energy industries and rare earth materials: Economic and financial issues," Energy Policy, Elsevier, vol. 66(C), pages 53-61.
    10. Ratti, Ronald A & Vespignani, Joaquin L., 2012. "Crude Oil Prices: China’s Influence Over 1996-2011," Working Papers 15728, University of Tasmania, School of Economics and Finance, revised 17 Dec 2012.
    11. Yen-Hsien Lee & Ya-Ling Huang & Chun-Yu Wu, 2013. "Conditional Jump Dynamics in the Stock Prices of Alternative Energy Companies," International Journal of Energy Economics and Policy, Econjournals, vol. 3(3), pages 288-296.
    12. 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.
    13. Bohl, Martin T. & Kaufmann, Philipp & Stephan, Patrick M., 2013. "From hero to zero: Evidence of performance reversal and speculative bubbles in German renewable energy stocks," Energy Economics, Elsevier, vol. 37(C), pages 40-51.
    14. Stavros Degiannakis & George Filis & Renatas Kizys, 2013. "Oil price shocks and stock market volatility: evidence from European data," Working Papers 161, Bank of Greece.
    15. Wang, Yudong & Wu, Chongfeng, 2012. "Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?," Energy Economics, Elsevier, vol. 34(6), pages 2167-2181.
    16. Martin Bohl & Philipp Kaufmann & Patrick Stephan, 2012. "From Hero to Zero: Evidence of Performance Reversal and Speculative Bubbles in German Renewable Energy Stocks," CQE Working Papers 2412, Center for Quantitative Economics (CQE), University of Muenster.
    17. Degiannakis, Stavros & Filis, George & Floros, Christos, 2013. "Oil and stock returns: Evidence from European industrial sector indices in a time-varying environment," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 26(C), pages 175-191.
    18. Асатуров К.Г. & Теплова Т.В., 2014. "Построение Коэффициентов Хеджирования Для Высоколиквидных Акций Российского Рынка На Основе Моделей Класс�," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 50(1), pages 37-54, янваÑ.

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