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Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models

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  • Agnolucci, Paolo
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    Abstract

    The WTI future contract quoted at the NYMEX is the most actively traded instrument in the energy sector. This paper compares the predictive ability of two approaches which can be used to forecast volatility: GARCH-type models where forecasts are obtained after estimating time series models, and an implied volatility model where forecasts are obtained by inverting one of the models used to price options. Although the main scope of the research discussed here is to evaluate which model produces the best forecast of volatility for the WTI future contract, evaluated according to statistical and regression-based criteria, we also investigate whether volatility of the oil futures are affected by asymmetric effects, whether parameters of the GARCH models are influenced by the distribution of the errors and whether allowing for a time-varying long-run mean in the volatility produces any improvement on the forecast obtained from GARCH models.

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    Bibliographic Info

    Article provided by Elsevier in its journal Energy Economics.

    Volume (Year): 31 (2009)
    Issue (Month): 2 (March)
    Pages: 316-321

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    Handle: RePEc:eee:eneeco:v:31:y:2009:i:2:p:316-321

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

    Related research

    Keywords: Oil price GARCH Implied volatility (IV);

    References

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    Citations

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    Cited by:
    1. Marc Gronwald, 2011. "A Characterization of Oil Price Behavior - Evidence from Jump Models," CESifo Working Paper Series 3644, CESifo Group Munich.
    2. El Hedi Arouri, Mohamed & Jouini, Jamel & Nguyen, Duc Khuong, 2011. "Volatility spillovers between oil prices and stock sector returns: Implications for portfolio management," Journal of International Money and Finance, Elsevier, vol. 30(7), pages 1387-1405.
    3. Marc Gronwald, 2009. "Jumps in Oil Prices- Evidence and Implications," Ifo Working Paper Series Ifo Working Paper No. 75, Ifo Institute for Economic Research at the University of Munich.
    4. Wei, Yu & Wang, Yudong & Huang, Dengshi, 2010. "Forecasting crude oil market volatility: Further evidence using GARCH-class models," Energy Economics, Elsevier, vol. 32(6), pages 1477-1484, November.
    5. Arouri, Mohamed El Hedi & Hammoudeh, Shawkat & Lahiani, Amine & Nguyen, Duc Khuong, 2012. "Long memory and structural breaks in modeling the return and volatility dynamics of precious metals," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(2), pages 207-218.
    6. He, Kaijian & Lai, Kin Keung & Yen, Jerome, 2011. "Value-at-risk estimation of crude oil price using MCA based transient risk modeling approach," Energy Economics, Elsevier, vol. 33(5), pages 903-911, September.
    7. Nomikos, Nikos K. & Pouliasis, Panos K., 2011. "Forecasting petroleum futures markets volatility: The role of regimes and market conditions," Energy Economics, Elsevier, vol. 33(2), pages 321-337, March.
    8. Mohamed El Hedi Arouri & Amine Lahiani & Duc Khuong Nguyen, 2013. "World gold prices and stock returns in China: insights for hedging and diversification strategies," Working Papers hal-00798038, HAL.
    9. Mohammadi, Hassan & Su, Lixian, 2010. "International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models," Energy Economics, Elsevier, vol. 32(5), pages 1001-1008, September.
    10. Wang, Tao & Yang, Jian, 2010. "Nonlinearity and intraday efficiency tests on energy futures markets," Energy Economics, Elsevier, vol. 32(2), pages 496-503, March.
    11. Vo, Minh, 2011. "Oil and stock market volatility: A multivariate stochastic volatility perspective," Energy Economics, Elsevier, vol. 33(5), pages 956-965, September.
    12. He, Angela W.W. & Kwok, Jerry T.K. & Wan, Alan T.K., 2010. "An empirical model of daily highs and lows of West Texas Intermediate crude oil prices," Energy Economics, Elsevier, vol. 32(6), pages 1499-1506, November.
    13. Bentes, Sonia R & Menezes, Rui, 2012. "On the predictive power of implied volatility indexes: A comparative analysis with GARCH forecasted volatility," MPRA Paper 42193, University Library of Munich, Germany.

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