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Modeling and forecasting the volatility of petroleum futures prices

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  • Kang, Sang Hoon
  • Yoon, Seong-Min

Abstract

We investigate volatility models and their forecasting abilities for three types of petroleum futures contracts traded on the New York Mercantile Exchange (West Texas Intermediate crude oil, heating oil #2, and unleaded gasoline) and suggest some stylized facts about the volatility of these futures markets, particularly in regard to volatility persistence (or long-memory properties). In this context, we examine the persistence of market returns and volatility simultaneously using the following ARFIMA–GARCH-class models: ARIMA–GARCH, ARFIMA–GARCH, ARFIMA–IGARCH, and ARFIMA–FIGARCH. Although the ARFIMA–FIGARCH model better captures long-memory properties of returns and volatility, the out-of-sample analysis indicates no unique model for all three types of petroleum futures contracts, suggesting that investors should be careful when measuring and forecasting the volatility (risk) of petroleum futures markets.

Suggested Citation

  • Kang, Sang Hoon & Yoon, Seong-Min, 2013. "Modeling and forecasting the volatility of petroleum futures prices," Energy Economics, Elsevier, vol. 36(C), pages 354-362.
  • Handle: RePEc:eee:eneeco:v:36:y:2013:i:c:p:354-362
    DOI: 10.1016/j.eneco.2012.09.010
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    1. Brunetti, Celso & Gilbert, Christopher L., 2000. "Bivariate FIGARCH and fractional cointegration," Journal of Empirical Finance, Elsevier, vol. 7(5), pages 509-530, December.
    2. Bollerslev, Tim & Ole Mikkelsen, Hans, 1996. "Modeling and pricing long memory in stock market volatility," Journal of Econometrics, Elsevier, vol. 73(1), pages 151-184, July.
    3. Gu, Rongbao & Chen, Hongtao & Wang, Yudong, 2010. "Multifractal analysis on international crude oil markets based on the multifractal detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(14), pages 2805-2815.
    4. Fan, Ying & Zhang, Yue-Jun & Tsai, Hsien-Tang & Wei, Yi-Ming, 2008. "Estimating 'Value at Risk' of crude oil price and its spillover effect using the GED-GARCH approach," Energy Economics, Elsevier, vol. 30(6), pages 3156-3171, November.
    5. Zanotti, Giovanna & Gabbi, Giampaolo & Geranio, Manuela, 2010. "Hedging with futures: Efficacy of GARCH correlation models to European electricity markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 20(2), pages 135-148, April.
    6. 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.
    7. 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.
    8. Arouri, Mohamed El Hédi & Lahiani, Amine & Lévy, Aldo & Nguyen, Duc Khuong, 2012. "Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models," Energy Economics, Elsevier, vol. 34(1), pages 283-293.
    9. John Elder & Apostolos Serletis, 2008. "Long memory in energy futures prices," Review of Financial Economics, John Wiley & Sons, vol. 17(2), pages 146-155.
    10. Fong, Wai Mun & See, Kim Hock, 2002. "A Markov switching model of the conditional volatility of crude oil futures prices," Energy Economics, Elsevier, vol. 24(1), pages 71-95, January.
    11. Robinson, P.M. & Henry, M., 1999. "Long And Short Memory Conditional Heteroskedasticity In Estimating The Memory Parameter Of Levels," Econometric Theory, Cambridge University Press, vol. 15(3), pages 299-336, June.
    12. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    13. Diebold, Francis X. & Inoue, Atsushi, 2001. "Long memory and regime switching," Journal of Econometrics, Elsevier, vol. 105(1), pages 131-159, November.
    14. Y. K. Tse, 2002. "Residual-based diagnostics for conditional heteroscedasticity models," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 358-374, June.
    15. Apostolos Serletis & Ioannis Andreadis, 2007. "Random Fractal Structures in North American Energy Markets," World Scientific Book Chapters, in: Quantitative And Empirical Analysis Of Energy Markets, chapter 18, pages 245-255, World Scientific Publishing Co. Pte. Ltd..
    16. 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.
    17. Engle, R. F. & Granger, C. W. J. (ed.), 1991. "Long-Run Economic Relationships: Readings in Cointegration," OUP Catalogue, Oxford University Press, number 9780198283393.
    18. Hammoudeh, Shawkat & Li, Huimin, 2004. "The impact of the Asian crisis on the behavior of US and international petroleum prices," Energy Economics, Elsevier, vol. 26(1), pages 135-160, January.
    19. Agnolucci, Paolo, 2009. "Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models," Energy Economics, Elsevier, vol. 31(2), pages 316-321, March.
    20. Berry Wilson & Reena Aggarwal & Carla Inclan, 1996. "Detecting volatility changes across the oil sector," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 16(3), pages 313-330, May.
    21. Hiemstra, Craig & Jones, Jonathan D., 1997. "Another look at long memory in common stock returns," Journal of Empirical Finance, Elsevier, vol. 4(4), pages 373-401, December.
    22. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    23. Tao Wang & Jingtao Wu & Jian Yang, 2008. "Realized volatility and correlation in energy futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 28(10), pages 993-1011, October.
    24. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    25. Hammoudeh, Shawkat & Li, Huimin & Jeon, Bang, 2003. "Causality and volatility spillovers among petroleum prices of WTI, gasoline and heating oil in different locations," The North American Journal of Economics and Finance, Elsevier, vol. 14(1), pages 89-114, March.
    26. 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.
    27. Param Silvapulle & Imad A. Moosa, 1999. "The relationship between spot and futures prices: Evidence from the crude oil market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 19(2), pages 175-193, April.
    28. Kasman, Adnan & Kasman, Saadet & Torun, Erdost, 2009. "Dual long memory property in returns and volatility: Evidence from the CEE countries' stock markets," Emerging Markets Review, Elsevier, vol. 10(2), pages 122-139, June.
    29. Conrad, C. & Karanasos, M., 2005. "On the inflation-uncertainty hypothesis in the USA, Japan and the UK: a dual long memory approach," Japan and the World Economy, Elsevier, vol. 17(3), pages 327-343, August.
    30. Conrad Christian & Karanasos Menelaos, 2005. "Dual Long Memory in Inflation Dynamics across Countries of the Euro Area and the Link between Inflation Uncertainty and Macroeconomic Performance," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-38, December.
    31. Lamoureux, Christopher G & Lastrapes, William D, 1990. "Persistence in Variance, Structural Change, and the GARCH Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 225-234, April.
    32. Sadorsky, Perry, 2006. "Modeling and forecasting petroleum futures volatility," Energy Economics, Elsevier, vol. 28(4), pages 467-488, July.
    33. Lin, Sharon Xiaowen & Tamvakis, Michael N., 2001. "Spillover effects in energy futures markets," Energy Economics, Elsevier, vol. 23(1), pages 43-56, January.
    34. Lee, Yen-Hsien & Hu, Hsu-Ning & Chiou, Jer-Shiou, 2010. "Jump dynamics with structural breaks for crude oil prices," Energy Economics, Elsevier, vol. 32(2), pages 343-350, March.
    35. Aloui, Chaker & Mabrouk, Samir, 2010. "Value-at-risk estimations of energy commodities via long-memory, asymmetry and fat-tailed GARCH models," Energy Policy, Elsevier, vol. 38(5), pages 2326-2339, May.
    36. Wang, Yudong & Liu, Li, 2010. "Is WTI crude oil market becoming weakly efficient over time?: New evidence from multiscale analysis based on detrended fluctuation analysis," Energy Economics, Elsevier, vol. 32(5), pages 987-992, September.
    37. Huang, Bwo-Nung & Yang, C.W. & Hwang, M.J., 2009. "The dynamics of a nonlinear relationship between crude oil spot and futures prices: A multivariate threshold regression approach," Energy Economics, Elsevier, vol. 31(1), pages 91-98, January.
    38. Tabak, Benjamin M. & Cajueiro, Daniel O., 2007. "Are the crude oil markets becoming weakly efficient over time? A test for time-varying long-range dependence in prices and volatility," Energy Economics, Elsevier, vol. 29(1), pages 28-36, January.
    39. Kang, Sang Hoon & Yoon, Seong-Min, 2007. "Long memory properties in return and volatility: Evidence from the Korean stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 385(2), pages 591-600.
    40. Kang, Sang Hoon & Kang, Sang-Mok & Yoon, Seong-Min, 2009. "Forecasting volatility of crude oil markets," Energy Economics, Elsevier, vol. 31(1), pages 119-125, January.
    41. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    More about this item

    Keywords

    DM test; Forecasting ability; Long memory; Persistence; Petroleum futures;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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