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Forecasting Nonlinear Crude Oil Futures Prices

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  • Saeed Moshiri
  • Faezeh Foroutan

Abstract

The movements in oil prices are very complex and, therefore, seem to be unpredictable. However, one of the main challenges facing econometric models is to forecast such seemingly unpredictable economic series. Traditional linear structural models have not been promising when used for oil price forecasting. Although linear and nonlinear time series models have performed much better in forecasting oil prices, there is still room for improvement. If the data generating process is nonlinear, applying linear models could result in large forecast errors. Model specification in nonlinear modeling, however, can be very case dependent and time-consuming.In this paper, we model and forecast daily crude oil futures prices from 1983 to 2003, listed in NYMEX, applying ARIMA and GARCH models. We then test for chaos using embedding dimension, BDS(L), Lyapunov exponent, and neural networks tests. Finally, we set up a nonlinear and flexible ANN model to forecast the series. Since the test results indicate that crude oil futures prices follow a complex nonlinear dynamic process, we expect that the ANN model will improve forecasting accuracy. A comparison of the results of the forecasts among different models confirms that this is indeed the case.

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Bibliographic Info

Article provided by International Association for Energy Economics in its journal The Energy Journal.

Volume (Year): Volume 27 (2006)
Issue (Month): Number 4 ()
Pages: 81-96

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Handle: RePEc:aen:journl:2006v27-04-a04

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Cited by:
  1. Chantziara, Thalia & Skiadopoulos, George, 2008. "Can the dynamics of the term structure of petroleum futures be forecasted? Evidence from major markets," Energy Economics, Elsevier, vol. 30(3), pages 962-985, May.
  2. Barkoulas, John T. & Chakraborty, Atreya & Ouandlous, Arav, 2012. "A metric and topological analysis of determinism in the crude oil spot market," Energy Economics, Elsevier, vol. 34(2), pages 584-591.
  3. Wang, Tao & Yang, Jian, 2010. "Nonlinearity and intraday efficiency tests on energy futures markets," Energy Economics, Elsevier, vol. 32(2), pages 496-503, March.
  4. Andrea Bastianin & Matteo Manera & Anil Markandya & Elisa Scarpa, 2011. "Oil Price Forecast Evaluation with Flexible Loss Functions," Working Papers 2011.91, Fondazione Eni Enrico Mattei.
  5. Zhang, Xun & Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2009. "Estimating the impact of extreme events on crude oil price: An EMD-based event analysis method," Energy Economics, Elsevier, vol. 31(5), pages 768-778, September.
  6. 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.
  7. Bernard, Jean-Thomas & Khalaf, Lynda & Kichian, Maral & McMahon, S├ębastien, 2008. "Oil Prices: Heavy Tails, Mean Reversion and the Convenience Yield," Cahiers de recherche 0801, GREEN.
  8. Matteo Manera & Chiara Longo & Anil Markandya & Elisa Scarpa, 2007. "Evaluating the Empirical Performance of Alternative Econometric Models for Oil Price Forecasting," Working Papers 2007.4, Fondazione Eni Enrico Mattei.

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