GARCH Models in Forecasting the Volatility of the World’s Oil Prices

  • Nguyen Trung Hung
  • Nguyen Ngoc Thach
  • Le Hoang Anh
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 760)

Abstract

This study was conducted to forecast the volatility of the world’s oil prices. Using the daily data of the WTI spot oil price collected from the US Energy Information Administration in the period from 01/02/1986 to 25/4/2016, estimation using models such as GARCH(1,1), EGARCH(1,1), GJR-GARCH(1,1) was made under 4 different distributions: normal distribution, Student’s t-distribution, generalized error distribution (GED), skewed Student’s t-distribution. The results show that the EGARCH(1,1) model with Student’s t-distribution provides the most accurate forecast. In addition, it is also shown that the volatility of crude oil price in the future can be predicted by the past volatility while crude oil price shock has a relatively small impact on oil price volatility.

References

  1. Bollerslev, T.: Generalized autoregressive heteroskedasticity. J. Econ. 31, 307–327 (1986)MathSciNetCrossRefMATHGoogle Scholar
  2. Bopp, A.E., Lady, G.M.: A comparison of petroleum futures versus spot prices as predictors of prices in the future. Energy Econ. 13, 274–282 (1991)CrossRefGoogle Scholar
  3. Day, T.E., Lewis, C.M.: Forecasting futures market volatility. J. Deriv. 1, 33–50 (1993)CrossRefGoogle Scholar
  4. Dufe, D., Gray, S.: Volatility in Energy Prices. Managing Energy Price Risk, pp. 39–55. Risk Publications, London (1995)Google Scholar
  5. Engle, R.F., McFadden, D.L.: ARCH models. In: Handbook of Econometrics, vol. IV, pp. 2961–3038. Elsevier Science (1994)Google Scholar
  6. Engle, R.F.: Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50, 987–1007 (1982)MathSciNetCrossRefMATHGoogle Scholar
  7. Glosten, L.R., Jagannathan, R., Runkle, D.E.: On the relation between the expected value and the volatility of the nominal excess return on stocks. J. Finan. 48(5), 1779–1801 (1993)CrossRefGoogle Scholar
  8. Sadorsky, P.: Modeling and forecasting petroleum futures volatility. Energy Econ. 28, 467–488 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Nguyen Trung Hung
    • 1
  • Nguyen Ngoc Thach
    • 2
  • Le Hoang Anh
    • 3
  1. 1.Chiang Mai UniversityChiang MaiThailand
  2. 2.Institute of Science and TechnologyBanking University of Ho Chi Minh CityHo Chi Minh CityVietnam
  3. 3.HCMC University of Food IndustryHo Chi Minh CityVietnam

Personalised recommendations