A comparative study on Box-Jenkins and Garch models in forecasting crude oil prices
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 s...
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Asian Network for Scientific Information
2011
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| Online Access: | http://umpir.ump.edu.my/id/eprint/24993/ http://umpir.ump.edu.my/id/eprint/24993/1/A%20comparative%20study%20on%20Box-Jenkins%20and%20Garch%20models%20in%20forecasting%20crude%20oil%20prices.pdf |
| _version_ | 1848822173393747968 |
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| author | Siti Roslindar, Yaziz Maizah Hura, Ahmad Chee Nian, Lee Noryanti, Muhammad |
| author_facet | Siti Roslindar, Yaziz Maizah Hura, Ahmad Chee Nian, Lee Noryanti, Muhammad |
| author_sort | Siti Roslindar, Yaziz |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T02:37:02Z |
| format | Article |
| id | ump-24993 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:37:02Z |
| publishDate | 2011 |
| publisher | Asian Network for Scientific Information |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-249932019-05-30T08:09:13Z http://umpir.ump.edu.my/id/eprint/24993/ A comparative study on Box-Jenkins and Garch models in forecasting crude oil prices Siti Roslindar, Yaziz Maizah Hura, Ahmad Chee Nian, Lee Noryanti, Muhammad Q Science (General) 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. Asian Network for Scientific Information 2011 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/24993/1/A%20comparative%20study%20on%20Box-Jenkins%20and%20Garch%20models%20in%20forecasting%20crude%20oil%20prices.pdf Siti Roslindar, Yaziz and Maizah Hura, Ahmad and Chee Nian, Lee and Noryanti, Muhammad (2011) A comparative study on Box-Jenkins and Garch models in forecasting crude oil prices. Journal of Applied Sciences, 11 (7). pp. 1129-1135. ISSN 1812-5654. (Published) https://scialert.net/abstract/?doi=jas.2011.1129.1135 |
| spellingShingle | Q Science (General) Siti Roslindar, Yaziz Maizah Hura, Ahmad Chee Nian, Lee Noryanti, Muhammad A comparative study on Box-Jenkins and Garch models in forecasting crude oil prices |
| title | A comparative study on Box-Jenkins and Garch models in forecasting crude oil prices |
| title_full | A comparative study on Box-Jenkins and Garch models in forecasting crude oil prices |
| title_fullStr | A comparative study on Box-Jenkins and Garch models in forecasting crude oil prices |
| title_full_unstemmed | A comparative study on Box-Jenkins and Garch models in forecasting crude oil prices |
| title_short | A comparative study on Box-Jenkins and Garch models in forecasting crude oil prices |
| title_sort | comparative study on box-jenkins and garch models in forecasting crude oil prices |
| topic | Q Science (General) |
| url | http://umpir.ump.edu.my/id/eprint/24993/ http://umpir.ump.edu.my/id/eprint/24993/ http://umpir.ump.edu.my/id/eprint/24993/1/A%20comparative%20study%20on%20Box-Jenkins%20and%20Garch%20models%20in%20forecasting%20crude%20oil%20prices.pdf |