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Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models
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Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models

Author

Listed:
  • Aldo Levy

    (LIRSA - Laboratoire interdisciplinaire de recherche en sciences de l'action - CNAM - Conservatoire National des Arts et Métiers [CNAM])

  • M.H. Arouri
  • Amine Lahiani

    (LEO - Laboratoire d'économie d'Orleans [2008-2011] - UO - Université d'Orléans - CNRS - Centre National de la Recherche Scientifique)

  • Duc Khuong Nguyen

    (IPAG Business School)

Abstract

This paper extends previous studies by investigating the relevance of structural breaks and long memory in modeling and forecasting the conditional volatility of oil spot and futures prices using a variety of GARCH-type models. Our results can be summarized as follows. First, we provide evidence of parameter instability in five out of nine GARCH-based conditional volatility processes for energy prices. Second, long memory is effectively present in all the series considered and a FIGARCH model seems to better fit the data, but the degree of volatility persistence diminishes significantly after adjusting for structural breaks. Finally, the out-of-sample analysis shows that volatility models accommodating instability and long memory characteristics of the data provide the best volatility forecasts for most cases.

Suggested Citation

  • Aldo Levy & M.H. Arouri & Amine Lahiani & Duc Khuong Nguyen, 2012. "Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models," Post-Print halshs-01279906, HAL.
    • Handle: RePEc:hal:journl:halshs-01279906
    DOI: 10.1016/j.eneco.2011.10.015
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    as
    1. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
    2. Zhang, Dayong, 2008. "Oil shock and economic growth in Japan: A nonlinear approach," Energy Economics, Elsevier, vol. 30(5), pages 2374-2390, September.
    3. Boyer, M. Martin & Filion, Didier, 2007. "Common and fundamental factors in stock returns of Canadian oil and gas companies," Energy Economics, Elsevier, vol. 29(3), pages 428-453, May.
    4. Charles, Amélie & Darné, Olivier, 2009. "The efficiency of the crude oil markets: Evidence from variance ratio tests," Energy Policy, Elsevier, vol. 37(11), pages 4267-4272, November.
    5. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    6. Lardic, Sandrine & Mignon, Valérie, 2008. "Oil prices and economic activity: An asymmetric cointegration approach," Energy Economics, Elsevier, vol. 30(3), pages 847-855, May.
    7. Whitney K. Newey & Kenneth D. West, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(4), pages 631-653.
    8. 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.
    9. Hedi Arouri, Mohamed El & Khuong Nguyen, Duc, 2010. "Oil prices, stock markets and portfolio investment: Evidence from sector analysis in Europe over the last decade," Energy Policy, Elsevier, vol. 38(8), pages 4528-4539, August.
    10. West, Kenneth D. & Cho, Dongchul, 1995. "The predictive ability of several models of exchange rate volatility," Journal of Econometrics, Elsevier, vol. 69(2), pages 367-391, October.
    11. Choi, Kyongwook & Yu, Wei-Choun & Zivot, Eric, 2010. "Long memory versus structural breaks in modeling and forecasting realized volatility," Journal of International Money and Finance, Elsevier, vol. 29(5), pages 857-875, September.
    12. Basher, Syed A. & Sadorsky, Perry, 2006. "Oil price risk and emerging stock markets," Global Finance Journal, Elsevier, vol. 17(2), pages 224-251, December.
    13. Granger, Clive W. J. & Hyung, Namwon, 2004. "Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 399-421, June.
    14. Cologni, Alessandro & Manera, Matteo, 2008. "Oil prices, inflation and interest rates in a structural cointegrated VAR model for the G-7 countries," Energy Economics, Elsevier, vol. 30(3), pages 856-888, May.
    15. John Elder & Apostolos Serletis, 2008. "Long memory in energy futures prices," Review of Financial Economics, John Wiley & Sons, vol. 17(2), pages 146-155.
    16. 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.
    17. 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.
    18. Catalin Starica & Stefano Herzel & Tomas Nord, 2005. "Why does the GARCH(1,1) model fail to provide sensible longer- horizon volatility forecasts?," Econometrics 0508003, University Library of Munich, Germany.
    19. Regnier, Eva, 2007. "Oil and energy price volatility," Energy Economics, Elsevier, vol. 29(3), pages 405-427, May.
    20. Choi, Kyongwook & Zivot, Eric, 2007. "Long memory and structural changes in the forward discount: An empirical investigation," Journal of International Money and Finance, Elsevier, vol. 26(3), pages 342-363, April.
    21. Diebold, Francis X. & Inoue, Atsushi, 2001. "Long memory and regime switching," Journal of Econometrics, Elsevier, vol. 105(1), pages 131-159, November.
    22. 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.
    23. Thomas Mikosch & Cătălin Stărică, 2004. "Nonstationarities in Financial Time Series, the Long-Range Dependence, and the IGARCH Effects," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 378-390, February.
    24. Gonzalez-Rivera, Gloria & Lee, Tae-Hwy & Mishra, Santosh, 2004. "Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood," International Journal of Forecasting, Elsevier, vol. 20(4), pages 629-645.
    25. Jones, Charles M & Kaul, Gautam, 1996. "Oil and the Stock Markets," Journal of Finance, American Finance Association, vol. 51(2), pages 463-491, June.
    26. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    27. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    28. Ciner Cetin, 2001. "Energy Shocks and Financial Markets: Nonlinear Linkages," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 5(3), pages 1-11, October.
    29. Narayan, Paresh Kumar & Narayan, Seema, 2007. "Modelling oil price volatility," Energy Policy, Elsevier, vol. 35(12), pages 6549-6553, December.
    30. Cheong, Chin Wen, 2009. "Modeling and forecasting crude oil markets using ARCH-type models," Energy Policy, Elsevier, vol. 37(6), pages 2346-2355, June.
    31. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    32. 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.
    33. 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.
    34. Driesprong, Gerben & Jacobsen, Ben & Maat, Benjamin, 2008. "Striking oil: Another puzzle?," Journal of Financial Economics, Elsevier, vol. 89(2), pages 307-327, August.
    35. Plourde, André & Watkins, G. C., 1998. "Crude oil prices between 1985 and 1994: how volatile in relation to other commodities?," Resource and Energy Economics, Elsevier, vol. 20(3), pages 245-262, September.
    36. David E. Rapach & Jack K. Strauss, 2008. "Structural breaks and GARCH models of exchange rate volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(1), pages 65-90.
    37. Hillebrand, Eric, 2005. "Neglecting parameter changes in GARCH models," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 121-138.
    38. Sadorsky, Perry, 2006. "Modeling and forecasting petroleum futures volatility," Energy Economics, Elsevier, vol. 28(4), pages 467-488, July.
    39. Hamilton, James D, 1983. "Oil and the Macroeconomy since World War II," Journal of Political Economy, University of Chicago Press, vol. 91(2), pages 228-248, April.
    40. Kyongwook Choi & Shawkat Hammoudeh, 2009. "Long Memory in Oil and Refined Products Markets," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 97-116.
    41. 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.
    42. 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.
    43. Lee, Chien-Chiang & Lee, Jun-De, 2009. "Energy prices, multiple structural breaks, and efficient market hypothesis," Applied Energy, Elsevier, vol. 86(4), pages 466-479, April.
    44. Nandha, Mohan & Faff, Robert, 2008. "Does oil move equity prices? A global view," Energy Economics, Elsevier, vol. 30(3), pages 986-997, May.
    45. Y. K. Tse, 1998. "The conditional heteroscedasticity of the yen-dollar exchange rate," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(1), pages 49-55.
    46. 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.
    47. 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

    Oil markets; Volatility forecasting; Long memory; Structural breaks; GARCH-class models;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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