Does long memory matter in forecasting oil price volatility?

This study attempts to introduce an appropri¬¬ate model for modeling and forecasting Iran’s crude oil price volatility. Therefore, this hypothesis will be tested about whether long memory feature matters in forecasting the price of this commodity. For this purpose, using the Iran’s weekly crude oil price data, the long memory feature will be considered in the return and volatilities series, and the fractal markets hypothesis will also be examined about Iran’s oil market. In addition, from among the different conditional heteroscedasticity models, the best model for forecasting oil price volatilities will be selected based the forecasting error criterion. The main hypothesis of the study will be tested out using Clark-West test (2006). The results of our study confirmed the existence of long memory feature in both mean and variance equations of these series. But from among the conditional heteroscedasticity models, the ARFIMA-FIGARCH model was selected as the best model based on the Akaike and Schwarz information criteria (for modeling), and also the MSE criterion (for forecasting). Finally, the Clark-West test showed that the long memory feature is important in forecasting oil price volatilities.