Abstract
Crude oil price fluctuations affect almost every individual and activity on the planet. Forecasting the crude oil price is therefore an important concern especially in economic policy and financial circles as it enables stakeholders estimate crude oil price at a point in time. Autoregressive Integrated Moving Average has been an effective tool that has been used widely to model time series. Its limitation is the fact that it cannot model nonlinear systems sufficiently. This paper assesses the ability to build a robust forecasting model for the world crude oil price, Brent on the international market using a hybrid of two methods Autoregressive Integrated Moving Average and Polynomial Harmonic Group Method of Data Handling. Autoregressive Integrated Moving Average methodology is used to model the time series component with constant variance whilst the Polynomial Harmonic Group Method of Data Handling is used to model the harmonic Autoregressive Integrated Moving Average model residuals. Keywords: Autocorrelation, Harmonics, Residuals JEL Classifications: C18, C45, C51, C63, C87, O13 DOI: https://doi.org/10.32479/ijeep.7987
Highlights
Crude oil products fuel most vehicles in air, on water, and on land all over the world
The structure of the results flows as shown in Figure 1: Autoregressive integrated moving average (ARIMA)-phGMDH Methodology flow chart
Forecasting crude oil price is a proactive measure in the process of hedging against crude oil price risk, a major influence on most organisations’ sustainability
Summary
Crude oil products fuel most vehicles in air, on water, and on land all over the world. Changes in crude oil price is of significant interest to decision makers especially finance practitioners and commodity market participants. Crude oil price is the most complex and difficult to model because the changes are frequent, nonlinear, irregular and nonstationary. Accurate forecasting of the crude oil price time series is one of the greatest challenges and among the most important issues facing energy researchers and economists towards better decisions at several managerial levels. As a result, achieving reliable and highly accurate forecasting models to answer the uncertainties and complexities of crude oil price is necessary and important to policy makers
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