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A comparative study on box-jenkins and garch models in forecasting crude oil prices

Ahmad, Maizah Hura and Yaziz, Siti Roslindar and Muhammad, Noryanti and Lee, Chee Nian (2010) A comparative study on box-jenkins and garch models in forecasting crude oil prices. In: International Conference on Fundamental and Applied Sciences (ICFAS2010), 2010, Kuala Lumpur, Malaysia.

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Official URL: http://dx.doi.org/10.3923/jas.2011.1129.1135

Abstract

Crude oil is an important energy commodity to mankind. The fluctuation of crude oil prices has affected many related sectors and stock market indices. Hence, forecasting the crude oil prices is essential to avoid the future prices of the non-renewable natural resources to raise sky-rocket. In this study, daily West Texas Intermediate (WTI) crude oil prices data is obtained from Energy Information Administration (EIA) from 2nd January 1986 to 30th September 2009. This study uses the Box-Jenkins methodology and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) approach in analyzing the crude oil prices. ARIMA(1,2,1) and GARCH(1,1) are found to be the appropriate models under model identification, parameter estimation, diagnostic checking and forecasting future prices. In this study, the analyses are done with the aid of EViews software where the potential of this software in forecasting daily crude oil prices time series data is explored. Finally, using several measures, comparison performances between ARIMA(1, 2, 1) and GARCH(1,1) models are made. GARCH(1,1) is found to be a better model than ARIMA(1, 2, 1) model. Based on the study, it is concluded that ARIMA(1,2,1) model is able to produce good forecast based on a description of history patterns in crude oil prices. However, the GARCH(1,1) is the better model for daily crude oil prices due to its ability to capture the volatility by the non-constant of conditional variance.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Box-jenkins model, ARIMA, GARCH, crude oil prices
Subjects:Q Science
Divisions:Science
ID Code:23768
Deposited By: Liza Porijo
Deposited On:12 Jun 2012 04:55
Last Modified:12 Jun 2012 06:22

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