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Comparative Performance of Volatility Models for Oil Price

  • Afees A. Salisu

    (Department of Economics and Centre for Econometrics and Allied Research (CEAR), University of Ibadan, Ibadan, Nigeria.)

  • Ismail O. Fasanya

    (Department of Economics, Fountain University, Osogbo, Osun State, Nigeria)

In this paper, we compare the performance of volatility models for oil price using daily returns of WTI. The innovations of this paper are in two folds: (i) we analyse the oil price across three sub samples namely period before, during and after the global financial crisis, (ii) we also analyse the comparative performance of both symmetric and asymmetric volatility models for the oil price. We find that oil price was most volatile during the global financial crises compared to other sub samples. Based on the appropriate model selection criteria, the asymmetric GARCH models appear superior to the symmetric ones in dealing with oil price volatility. This finding indicates evidence of leverage effects in the oil market and ignoring these effects in oil price modelling will lead to serious biases and misleading results.

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Article provided by Econjournals in its journal International Journal of Energy Economics and Policy.

Volume (Year): 2 (2012)
Issue (Month): 3 ()
Pages: 167-183

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Handle: RePEc:eco:journ2:2012-03-9
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  1. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
  2. Oberndorfer, Ulrich, 2009. "Energy prices, volatility, and the stock market: Evidence from the Eurozone," Energy Policy, Elsevier, vol. 37(12), pages 5787-5795, December.
  3. Bina Cyrus & Vo Minh, 2007. "OPEC in the Epoch of Globalization: An Event Study of Global Oil Prices," Global Economy Journal, De Gruyter, vol. 7(1), pages 1-52, February.
  4. 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.
  5. Todd E. Clark & Michael W. McCracken, 1999. "Tests of equal forecast accuracy and encompassing for nested models," Research Working Paper 99-11, Federal Reserve Bank of Kansas City.
  6. Vo, Minh T., 2009. "Regime-switching stochastic volatility: Evidence from the crude oil market," Energy Economics, Elsevier, vol. 31(5), pages 779-788, September.
  7. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  8. Robert Engle, 2001. "GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 157-168, Fall.
  9. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
  10. Yang, C. W. & Hwang, M. J. & Huang, B. N., 2002. "An analysis of factors affecting price volatility of the US oil market," Energy Economics, Elsevier, vol. 24(2), pages 107-119, March.
  11. Brailsford, Timothy J. & Faff, Robert W., 1996. "An evaluation of volatility forecasting techniques," Journal of Banking & Finance, Elsevier, vol. 20(3), pages 419-438, April.
  12. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
  13. Mohamed El Hedi Arouri & Duc Khuong Nguyen & Amine Lahiani, 2010. "Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models," Working Papers hal-00507831, HAL.
  14. Kocenda, Evzen & Valachy, Juraj, 2006. "Exchange rate volatility and regime change: A Visegrad comparison," Journal of Comparative Economics, Elsevier, vol. 34(4), pages 727-753, December.
  15. Robert Engle, 2002. "New frontiers for arch models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 425-446.
  16. Askari, Hossein & Krichene, Noureddine, 2008. "Oil price dynamics (2002-2006)," Energy Economics, Elsevier, vol. 30(5), pages 2134-2153, September.
  17. 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.
  18. Hou, Aijun & Suardi, Sandy, 2012. "A nonparametric GARCH model of crude oil price return volatility," Energy Economics, Elsevier, vol. 34(2), pages 618-626.
  19. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
  20. Narayan, Paresh Kumar & Narayan, Seema, 2007. "Modelling oil price volatility," Energy Policy, Elsevier, vol. 35(12), pages 6549-6553, December.
  21. Lawrence R. Glosten & Ravi Jagannathan & David E. Runkle, 1993. "On the relation between the expected value and the volatility of the nominal excess return on stocks," Staff Report 157, Federal Reserve Bank of Minneapolis.
  22. Manishi Prasad & Peter Wahlqvist & Rich Shikiar & Ya-Chen Tina Shih, 2004. "A," PharmacoEconomics, Springer Healthcare | Adis, vol. 22(4), pages 225-244.
  23. Awartani, Basel M.A. & Corradi, Valentina, 2005. "Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries," International Journal of Forecasting, Elsevier, vol. 21(1), pages 167-183.
  24. Morana, Claudio, 2001. "A semiparametric approach to short-term oil price forecasting," Energy Economics, Elsevier, vol. 23(3), pages 325-338, May.
  25. 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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