Comparing the performance of GARCH model and gravitational search algorithm (GSA) in modeling and forecasting of spot oil price of Iran (adaptive expectations approach)

Forecasting of crude oil price plays a crucial role in optimization of production، marketing and market strategies. Furthermore، it plays a significant role in government’s policies، because the government sets and implements its policies not only according to the current situation but also according to short run and long run predictions of important economic variables like oil price. The main purpose of this study is modeling and forecasting spot oil price of Iran by using GARCH model and A Gravitational Search Algorithm. Performed forecasts of this study are based in static and out-of-sample forecasting and each subseries data is divided in to two parts: data for estimation and data for forecasting. The forecast horizon is next leading period and its length is one month. In this study the selected models for forecasting spot oil of Iran are GARCH (2،1) and a Cobb Douglas function which is functional of prices of 5 days ago. Finally، the performances of these models are compared. For comparison of these models MSE، RMSE، MAE، and MAPE criteria are used and the results indicate that except in MAPE criterion، the mentioned criteria are smaller for GARCH model in comparison to GSA algorithm.