Al-Gounmeein, Remal Shaher Hussien
(2022)
Hybridization Model For Capturing
Long Memory And Volatility Of
Brent Crude Oil Price Data.
PhD thesis, Universiti Sains Malaysia.
Abstract
The Brent crude oil price indices are typically nonlinear, nonstationary, and
non-normal behavior with a long memory and high heteroscedasticity; hence,
capturing the controlling properties of their changes is difficult. Subsequently, these
phenomena weaken the validity and the accuracy of the result of the forecasting
methods. Therefore, this study focuses on the hybridization method to capture long
memory behavior and heteroscedasticity in the dataset and improve Brent crude oil
price forecasting accuracy. Recently, the hybridization method for the autoregressive
fractionally integrated moving average (ARFIMA) model has been introduced as an
effective technique for overcoming the nonlinear, nonstationary, and non-normal
behavior with high heteroscedasticity in a time series dataset. ARFIMA hybridization
method presents several characteristics that other traditional methods do not have.
Thus, this thesis proposed three new models and employed 12 different techniques
based on combining and hybridizing the ARFIMA model with traditional forecasting
techniques to forecast the Brent crude oil price. The three new models, namely,
ARFIMA with the asymmetric power autoregressive conditional heteroscedasticity
(ARFIMA-APARCH), ARFIMA with the Glosten, Jagannathan, and Runkle
generalized autoregressive conditional heteroscedasticity (ARFIMA-GJRGARCH),
and ARFIMA with the component standard GARCH (ARFIMA-csGARCH) are
proposed. This proposal aims to obtain improved forecasting results and solve the
forecasting inaccuracy problem in oil price series.
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