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Volatility forecasting for crude oil futures

  • Massimiliano Marzo
  • Paolo Zagaglia

This article studies the forecasting properties of linear GARCH models for closing-day futures prices on crude oil, first position, traded in the New York Mercantile Exchange from January 1995 to November 2005. To account for fat tails in the empirical distribution of the series, we compare models based on the normal, Student's t and generalized exponential distribution. We focus on out-of-sample predictability by ranking the models according to a large array of statistical loss functions. The results from the tests for predictive ability show that the GARCH-G model fares best for short horizons from 1 to 3 days ahead. For horizons from 1 week ahead, no superior model can be identified. We also consider out-of-sample loss functions based on value-at-risk that mimic portfolio managers and regulators' preferences. Exponential GARCH models display the best performance in this case. The swings in oil prices that gave investors and traders whiplash in 2004 are not preventing new investors from rushing into oil and other energy-related commodities this year. (…) Ultimately, the rising number of speculator could lead to even more price volatility in 2005, pushing the highs higher and the lows lower. (…) After a generation in the wilderness, the oil futures that are used to make a bet on oil prices have become a bona fide investment, said Charles O'Donnell, who manages Lake Asset Management, a small energy fund based in London. Heather Timmons, The New York Times1

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Article provided by Taylor & Francis Journals in its journal Applied Economics Letters.

Volume (Year): 17 (2010)
Issue (Month): 16 ()
Pages: 1587-1599

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Handle: RePEc:taf:apeclt:v:17:y:2010:i:16:p:1587-1599
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  1. Marcucci Juri, 2005. "Forecasting Stock Market Volatility with Regime-Switching GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-55, December.
  2. Gita Persand & Chris Brooks, 2003. "Volatility forecasting for risk management," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(1), pages 1-22.
  3. Pesaran, M.H. & Timmermann, A., 1990. "A Simple Non-Parametric Test Of Predictive Performance," Papers 29, California Los Angeles - Applied Econometrics.
  4. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
  5. Sadorsky, Perry, 2006. "Modeling and forecasting petroleum futures volatility," Energy Economics, Elsevier, vol. 28(4), pages 467-488, July.
  6. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
  7. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  8. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-47, August.
  9. 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.
  10. Bollerslev, T. & Ghysels, E., 1994. "Periodic Autoregressive Conditional Heteroskedasticity," Cahiers de recherche 9408, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  11. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
  12. Asger Lunde & Peter Reinhard Hansen, 2001. "A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?," Working Papers 2001-04, Brown University, Department of Economics.
  13. Ewing, Bradley T. & Malik, Farooq & Ozfidan, Ozkan, 2002. "Volatility transmission in the oil and natural gas markets," Energy Economics, Elsevier, vol. 24(6), pages 525-538, November.
  14. Fleming, Jeff & Ostdiek, Barbara, 1999. "The impact of energy derivatives on the crude oil market," Energy Economics, Elsevier, vol. 21(2), pages 135-167, April.
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