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Forecasting the return volatility of energy prices: A GARCH MIDAS approach

Author

Listed:
  • Afees A. Salisu

    (Centre for Econometric and Allied Research, University of Ibadan)

  • Raymond Swaray

    (Economics Subject Group, University of Hull Business, University of Hull, Cottingham Road, UK)

Abstract

This paper offers an extension to the literature on energy prices by forecasting the return volatility of these prices using the GARCH-MIDAS approach. In addition to the realized volatility, it also evaluates the predictability of relevant macroeconomic information such as industrial growth and consumer prices (with and without energy components) in the predictive model for the return volatility of energy prices. The analyses are distinctly conducted for full-sample, pre-GFC and post-GFC periods. On average, the findings support the inclusion of these macroeconomic information particularly output growth and realized volatility as they yield good in-sample and outof- sample predictability results for the return volatility. However, the paper finds contrasting evidence between the pre-GFC and post-GFC periods.

Suggested Citation

  • Afees A. Salisu & Raymond Swaray, 2017. "Forecasting the return volatility of energy prices: A GARCH MIDAS approach," Working Papers 029, Centre for Econometric and Allied Research, University of Ibadan.
    • Handle: RePEc:cui:wpaper:0029
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    References listed on IDEAS

    as
    1. Ziel, Florian & Steinert, Rick & Husmann, Sven, 2015. "Efficient modeling and forecasting of electricity spot prices," Energy Economics, Elsevier, vol. 47(C), pages 98-111.
    2. Karali, Berna & Ramirez, Octavio A., 2014. "Macro determinants of volatility and volatility spillover in energy markets," Energy Economics, Elsevier, vol. 46(C), pages 413-421.
    3. Alanoud Al-Maadid & Guglielmo Maria Caporale & Fabio Spagnolo & Nicola Spagnolo, 2017. "Spillovers between food and energy prices and structural breaks," International Economics, CEPII research center, issue 150, pages 1-18.
    4. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    5. Pan, Zhiyuan & Wang, Yudong & Wu, Chongfeng & Yin, Libo, 2017. "Oil price volatility and macroeconomic fundamentals: A regime switching GARCH-MIDAS model," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 130-142.
    6. Narayan, Paresh Kumar & Liu, Ruipeng, 2015. "A unit root model for trending time-series energy variables," Energy Economics, Elsevier, vol. 50(C), pages 391-402.
    7. Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
    8. Manera, Matteo & Nicolini, Marcella & Vignati, Ilaria, 2016. "Modelling futures price volatility in energy markets: Is there a role for financial speculation?," Energy Economics, Elsevier, vol. 53(C), pages 220-229.
    9. Narayan, Paresh Kumar & Gupta, Rangan, 2015. "Has oil price predicted stock returns for over a century?," Energy Economics, Elsevier, vol. 48(C), pages 18-23.
    10. Xian, Hui & Colson, Gregory & Karali, Berna & Wetzstein, Michael, 2017. "Do nonrenewable-energy prices affect renewable-energy volatility? The case of wood pellets," Journal of Forest Economics, Elsevier, vol. 28(C), pages 42-48.
    11. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    12. Serletis, Apostolos & Xu, Libo, 2016. "Volatility and a century of energy markets dynamics," Energy Economics, Elsevier, vol. 55(C), pages 1-9.
    13. Koto, Prosper Senyo, 2015. "Are retail prices of ethanol, gasoline and natural gas in the midwest cointegrated? An asymmetric threshold cointegration analysis," Journal of Economics and Business, Elsevier, vol. 77(C), pages 79-93.
    14. Omri, Anis, 2014. "An international literature survey on energy-economic growth nexus: Evidence from country-specific studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 951-959.
    15. Ghysels, Eric & Kvedaras, Virmantas & Zemlys, Vaidotas, 2016. "Mixed Frequency Data Sampling Regression Models: The R Package midasr," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i04).
    16. Hossein Asgharian & Ai Jun Hou & Farrukh Javed, 2013. "The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH‐MIDAS Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(7), pages 600-612, November.
    17. Valadkhani, Abbas & Smyth, Russell, 2017. "How do daily changes in oil prices affect US monthly industrial output?," Energy Economics, Elsevier, vol. 67(C), pages 83-90.
    18. Koirala, Krishna H. & Mishra, Ashok K. & D'Antoni, Jeremy M. & Mehlhorn, Joey E., 2015. "Energy prices and agricultural commodity prices: Testing correlation using copulas method," Energy, Elsevier, vol. 81(C), pages 430-436.
    19. Apergis, Nicholas & Baruník, Jozef & Lau, Marco Chi Keung, 2017. "Good volatility, bad volatility: What drives the asymmetric connectedness of Australian electricity markets?," Energy Economics, Elsevier, vol. 66(C), pages 108-115.
    20. Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Filis, George, 2014. "Dynamic spillovers of oil price shocks and economic policy uncertainty," Energy Economics, Elsevier, vol. 44(C), pages 433-447.
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    More about this item

    Keywords

    GARCH-MIDAS; energy prices; return volatility; realized volatility; industrial production; inflation;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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