The Performance of Hybrid ARIMA-GARCH Modeling and Forecasting Oil Price
Modeling and forecasting oil prices is an important issue for many researchers. One of the methods used in forecasting oil prices is Box-Jenkins methodology through ARIMA models. Although these models provide accurate forecasting over a short time period, they are not able to handle the volatility and nonlinearity presented on data series. For this reason, on this paper we examine a hybrid ARIMA-GARCH model in order to forecast the volatility in the return of oil prices. Moreover, on this paper, the Box-Cox transformation is used for data smoothing for the stabilization of variance and reduction of heteroscedasticity. Parameters' estimation in the hybrid ARIMA-GARCH model is employed by ML (Maximum Likelihood) method using the steps of Marquardt's algorithm (1963) and Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm for optimization. The results of the paper showed that the hybridation of ARIMA(33,0,14)-GARCH(1,2) model following normal distribution is the most suitable for forecasting the returns of oil prices. Finally, we use both the dynamic and static procedure for forecasting. The results showed that the static procedure provides with better forecasting than the dynamic.
Keywords: ARIMA, GARCH, oil price forecasting, hybrid ARIMA-GARCH, Box-Cox transformation
JEL Classifications: C33, O13, Q43