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:	Lee, Chee Nian
Format:	Thesis
Language:	English
Published:	2009
Subjects:	HG Finance
Online Access:	http://eprints.utm.my/id/eprint/12354/6/LeeCheeNianMFS2009.pdf
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spelling	my-utm-ep.123542017-09-17T06:52:58Z Application of Arima and Garch models in forecasting crude oil prices 2009-11 Lee, Chee Nian HG Finance 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. 2009-11 Thesis http://eprints.utm.my/id/eprint/12354/ http://eprints.utm.my/id/eprint/12354/6/LeeCheeNianMFS2009.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Science Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	HG Finance
spellingShingle	HG Finance Lee, Chee Nian Application of Arima and Garch models in forecasting crude oil prices
description	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.
format	Thesis
qualification_level	Master's degree
author	Lee, Chee Nian
author_facet	Lee, Chee Nian
author_sort	Lee, Chee Nian
title	Application of Arima and Garch models in forecasting crude oil prices
title_short	Application of Arima and Garch models in forecasting crude oil prices
title_full	Application of Arima and Garch models in forecasting crude oil prices
title_fullStr	Application of Arima and Garch models in forecasting crude oil prices
title_full_unstemmed	Application of Arima and Garch models in forecasting crude oil prices
title_sort	application of arima and garch models in forecasting crude oil prices
granting_institution	Universiti Teknologi Malaysia, Faculty of Science
granting_department	Faculty of Science
publishDate	2009
url	http://eprints.utm.my/id/eprint/12354/6/LeeCheeNianMFS2009.pdf
_version_	1747814927561654272

Application of Arima and Garch models in forecasting crude oil prices

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