Abstract
Crude oil is one of the most important energy commodities for various sectors. Changes in crude oil prices will have an impact on oil-related sectors, and even on the stock price index. Therefore, the prediction of crude oil prices needs to be done to avoid the future prices of these non-renewable natural resources to increase dramatically. In this paper, the prediction of crude oil prices is carried out using the Auto-Regressive Integrated Moving Average (ARIMA) and Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) models. The data used for forecasting are Indonesian Crude Price (ICP) crude oil data for the period January 2005 to November 2012. The results show that the data analyzed follows the ARIMA(1,2,1)-GARCH(0,3) model, and the crude oil price forecast for December 2012 is 105.5528 USD per barrel. The prediction results of crude oil prices are expected to be important information for all sectors related to crude oil.
Highlights
The price of Indonesian crude oil fluctuates with the development of world crude oil prices
The purpose of this paper is to estimate the mean model with Auto-Regressive Integrated Moving Average (ARIMA) modeling and variance model with Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) modeling and using the model to predict the price of Indonesian crude oil (ICP) in the coming month
Lee concluded that the ARIMA(1,2,1) model can produce accurate estimates based on the information patterns in the history of crude oil prices
Summary
The price of Indonesian crude oil fluctuates with the development of world crude oil prices. The significant increase in crude oil prices is certainly not expected by many governments in the world including the crude oil-producing countries (Kulkarni & Haidar, 2009). For importing countries, this increase will disrupt economic growth due to high inflation. We need a model to predict future prices (Rosch & Schmidbauer, 2011; Yu et al, 2008). Time series analysis and forecasting (prediction) have become research material in various fields (Abledu & Kobina, 2012). Several time series models can be used to estimate and predict crude oil prices. This paper discusses the use of the Auto-Regressive Integrated Moving Average (ARIMA) model and the General Auto-Regressive Conditional Heteroscedasticity (GARCH) model
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