Forecasting the Volatility of OPEC Oil Prices using Fractionally Integrated GARCH models

Abstract

Some time series are characterized by their great volatility over time, especially time series related to the movement of the economy, and those related to the change in stock prices or the movement of financial transactions and stock markets, which are characterized by being non- stationary over time due to the change in the behavior of observations, making them suffer from the problem of  Heteroscedasticity . The paper aims the use of predictive models that a time series can adapt to with large fluctuations and with long memory over time, a number of important models used to deal with FIGARCH time series when the error distribution follows the t-distribution were studied and reviewed, which were used For the first time by Researcher Engel in 1982 and developed by other researchers, the characteristics of these models were reviewed and applied for the purpose of forecasting daily oil prices according to the prices approved by OPEC for the period from 2004 to 2022, where the practical analysis of oil price data showed that the best prediction model is the ARMA(2,2)-FIGARCH(1,d,2) model in which the error follows the t-distribution, and the best predictor performance is out of sample .