Application of ARIMA-GARCH Model for Prediction of Indonesian Crude Oil Prices

Sukono Sukono; Emah Suryamah; Fujika Novinta S

doi:10.47194/orics.v1i1.21

Authors

Sukono Sukono
sukono@unpad.ac.id
Emah Suryamah
Fujika Novinta S

Keywords:

Indonesian crude oil prices, predictions, ARIMA models, GARCH models.

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.

References

Abledu, G.K.,& Kobina, A. (2012). Stochastic Forecasting and Modeling of Volatility Oil Prices in Ghana using ARIMA Time Series Model. European Journal of Business and Management,4(16).

Bosler, F.T. (2010). Models For Oil Price Production and Forecasting. A Thesis. Master of Science in Applied Mathematics, San Diego State University.

Chand, S., Kamal, S.,& Ali, I. (2012). Modeling and Volatility Analysis of Share Prices Using ARCH and GARCH Models. World Applied Sciences Journal, 19(1), 77-82.

Jhohura, F.T.,& Rayhan, M.I. (2012). An Assessment of Renewable Energy in Bangladesh through ARIMA, Holtâ€™s, ARCH-GARCH Models. Dhaka Univ. J. Sci.,60(2), 159-162.

Kulkarni, S.,& Haidar, I. (2009). Forecasting Model for Crude Oil Price Using Artificial Neural Networks and Commodity Futures Prices.International Journal of Computer Science and Information Security, 2(1).

Lee, C. N. (2009). Application of ARIMA and GARCH Models in Forecasting Crude Oil Prices. A dissertation. Faculty of Science, Universiti Teknologi Malaysia.

Makiel, K. (2012). ARIMA-GARCH Models in Estimating Market Risk Using Value at Risk for the WIG20 Index. Financial Internet Quarterly (e-Finanse), 8(2).

Rosadi, D. (2012). Ekonometrika dan Analisis Runtun Waktu Terapan dengan Eviews.Yogyakarta: Penerbit ANDI.

Rosch, A.,& Schmidbauer, H. (2011). Crude Oil Spot Prices and the Marketâ€™s Perception of Inventory News. Working Paper. FOM University of Applied Sciences, Munich, Germany, & Ideal Analytix, Singapore.

Sukono, Subanar, & Rosadi, D. (2011). Pengukuran VaR Dengan Volatilitas Tak Konstan dan Efek Long Memory. Disertasi. Program Studi S3 Statistika, Jurusan Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Gajah Mada, Yogyakarta.

Tsay, R.S. (2005). Analysis of Financial Time Series, Second Edition. New Jersey: John Wiley & Sons.

Yu, L., Wang, S.,& Lai, K.K. (2008). Forecasting Crude Oil Price with an EMD-Based Neural Network Ensemble Learning Paradigm.Energy Economics,30, 2623â€“2635.