Application of Arima and Garch models in forecasting crude oil prices
Crude oil is an important energy commodity to mankind. Several causes have made crude oil prices to be volatile. 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-...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2009
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/12354/6/LeeCheeNianMFS2009.pdf |
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| Summary: | Crude oil is an important energy commodity to mankind. Several causes have made crude oil prices to be volatile. 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 WTI crude oil prices data is obtained from Energy Information Administration (EIA) from 2nd January 1986 to 30th September 2009. We use the Box-Jenkins methodology and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) approach in forecasting the crude oil prices. An Autoregressive Integrated Moving Average (ARIMA) model is set as the benchmark model. We found ARIMA(1,2,1) and GARCH(1,1) are 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, we conclude that ARIMA(1,2,1) model is able to produce accurate 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. |
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