Shabani, M., Gharneh, N., Esfahanipour, A. (2017). Comparing the performance of GARCH (p,q) models with different methods of estimation for forecasting crude oil market volatility. Journal of Industrial and Systems Engineering, 9(4), 80-92.

Maryam Shabani; Naser Shams Gharneh; Akbar Esfahanipour. "Comparing the performance of GARCH (p,q) models with different methods of estimation for forecasting crude oil market volatility". Journal of Industrial and Systems Engineering, 9, 4, 2017, 80-92.

Shabani, M., Gharneh, N., Esfahanipour, A. (2017). 'Comparing the performance of GARCH (p,q) models with different methods of estimation for forecasting crude oil market volatility', Journal of Industrial and Systems Engineering, 9(4), pp. 80-92.

Shabani, M., Gharneh, N., Esfahanipour, A. Comparing the performance of GARCH (p,q) models with different methods of estimation for forecasting crude oil market volatility. Journal of Industrial and Systems Engineering, 2017; 9(4): 80-92.

Comparing the performance of GARCH (p,q) models with different methods of estimation for forecasting crude oil market volatility

^{1}Department of Industrial Engineering and Management Systems, Amirkabir University of Technology

^{2}Department of Industrial engineering and Management Systems, Amirkabir University of Technology

Abstract

The use of GARCH models to characterize crude oil price volatility is widely observed in the empirical literature. In this paper the efficiency of six univariate GARCH models and two methods of estimation the parameters for forecasting oil price volatility are examined and the best method for forecasting crude oil price volatility of Brent market is determined. All the examined models in this paperbelong to the univariate time series family. This article investigates and compares the efficiency of volatility models for crude oil markets. The four years out-of-sample volatility forecasts of the GARCH models are evaluated using the superior predictive ability test with more loss function. The results find that GARCH (1,1) model can outperform all of the other models for the crude oil price of Brent market across different loss functions. Four different measures are used to evaluate the forecasting accuracy of the models. Then, two methods of estimation the parameters of GARCH models, for forecasting oil price volatility are compared. The results suggest that, maximum likelihood estimation (MLE) gives better results for estimation than generalized method of moments (GMM) in this study.

Ã, M.S. & Shavvalpour, S., 2006. Energy risk management and value at risk modeling. , 34, pp.3367–3373.

Adrangi, B., Chatrath, A. & Raffiee, K., 2001. Alaska North Slope crude oil price and the behavior of diesel prices in California.

Agnolucci, P., 2009. Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models. Energy Economics, 31(2), pp.316–321. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0140988308001655.

Alberg, D., Shalit, H. & Yosef, R., 2008. Estimating stock market volatility using asymmetric GARCH models. Applied Financial Economics, 18(15), pp.1201–1208.

Andersen, T.G. et al., 2006. Chapter 15 Volatility and Correlation Forecasting. Handbook of Economic Forecasting, 1(05), pp.777–878.

Armstrong, J. & Fildes, R., 1995. On the selection of error measures for comparisons among forecasting methods. Journal of Forecasting, 14(August 1994), pp.67–71. Available at: http://onlinelibrary.wiley.com/doi/10.1002/for.3980140106/abstract.

Baillie, R.T., Bollerslev, T. & Mikkelsen, H.O., 1996. Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 74(1), pp.3–30.

Breusch, T. et al., 1982. A g e n e r a l a p p r o a c h to l a g r a n g e m u l t i p l i e r m o d e l. , 20, pp.83–104.

Cabedo, J.D. & Moya, I., 2003. Estimating oil price “ Value at Risk ” using the historical simulation approach. , 25, pp.239–253.

Chan, J.C.C. & Grant, A.L., 2016. Modeling energy price dynamics: GARCH versus stochastic volatility. Energy Economics, 54, pp.182–189. Available at: http://dx.doi.org/10.1016/j.eneco.2015.12.003.

Ding, Z., Granger, C.W.J. & Engle, R.F., 1993. A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1(1), pp.83–106.

Hall, A., 2009. Generalized method of moments. Encyclopedia of quantitative Finance, pp.1–15. Available at: http://personalpages.manchester.ac.uk/staff/Alastair.Hall/GMM_EQF_100309.pdf.

Hammad, M. et al., 2012. Investigating Stress and Employee Performance in Traffic Police. International Proceedings of Economics Development and Research, 55(28), pp.141–144.

Hou, A. & Suardi, S., 2012. A nonparametric GARCH model of crude oil price return volatility. Energy Economics, 34(2), pp.618–626. Available at: http://dx.doi.org/10.1016/j.eneco.2011.08.004.

Jiang, J., Marsh, T.L. & Tozer, P.R., 2015. Policy induced price volatility transmission: Linking the U.S. crude oil, corn and plastics markets. Energy Economics, 52, pp.217–227. Available at: http://dx.doi.org/10.1016/j.eneco.2015.10.008.

Johansen, S. & Zivot, E., 2009. Handbook of Financial Time Series, Available at: http://www.springerlink.com/index/10.1007/978-3-540-71297-8.

Joon, S. & Cho, H., 2013. Forecasting carbon futures volatility using GARCH models with energy volatilities. , 40, pp.207–221.

Kang, S.H., Kang, S.M. & Yoon, S.M., 2009. Forecasting volatility of crude oil markets. Energy Economics, 31(1), pp.119–125. Available at: http://dx.doi.org/10.1016/j.eneco.2008.09.006.

Mohammadi, H. & Su, L., 2010. International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models. Energy Economics, 32(5), pp.1001–1008. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0140988310000654.

Nelson, D.B., 1991. Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), pp.347–370. Available at: http://www.jstor.org/stable/2938260.

Olga, E. & Serletis, A., 2014. Energy markets volatility modelling using GARCH ☆ , ☆☆. , 43, pp.264–273.

Princ, P. & Školuda, V., 2012. A Note on GARCH ( 1 , 1 ) Estimation via Different Estimation Methods. Methods, pp.81–85.

Properties, L.S. et al., 2014. Large Sample Properties of Generalized Method of Moments Estimators Author(s): Lars Peter Hansen Source: , 50(4), pp.1029–1054.

Sadorsky, P., 2006. Modeling and forecasting petroleum futures volatility. Energy Economics, 28(4), pp.467–488. Available at: http://www.sciencedirect.com/science/article/pii/S014098830600048X.

Tobergte, D.R. & Curtis, S., 2013. No Title No Title. Journal of Chemical Information and Modeling, 53(9), pp.1689–1699.

Wang, Y., Wu, C. & Wei, Y., 2011. Can GARCH-class models capture long memory in WTI crude oil markets? Economic Modelling, 28(3), pp.921–927. Available at: http://dx.doi.org/10.1016/j.econmod.2010.11.002.

Wei, Y., Wang, Y. & Huang, D., 2010. Forecasting crude oil market volatility: Further evidence using GARCH-class models. Energy Economics, 32(6), pp.1477–1484. Available at: http://dx.doi.org/10.1016/j.eneco.2010.07.009.

Yu, J., 2002. Forecasting volatility in the New Zealand stock market. Applied Financial Economics, 12(3), pp.193–202.

Zivot, E., 2008. Practical Issues in the Analysis of Univariate GARCH Some Stylized Facts of Asset Returns. Time, pp.1–41.