Application of Markov Model in Crude Oil Price Forecasting

Nuhu Isah, Abdul Talib Bon

Abstract

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 rise. In this study, daily crude oil prices data was obtained from WTI dated 2 January to 29 May 2015. We used Markov Model (MM) approach in forecasting the crude oil prices. In this study, the analyses were done using EViews and Maple software where the potential of this software in forecasting daily crude oil prices time series data was explored. Based on the study, we concluded that MM model is able to produce accurate forecast based on a description of history patterns in crude oil prices.



Keywords


forecasting; crude oil; price; Markov model

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References


1. Xiu, S., & Shahbazi, A. (2012). Bio-oil production and upgrading research: A review. Renewable and Sustainable Energy Reviews, 16(7), 4406–4414.

[Google Scholar] [CrossRef]

2. Xie, W, Yu, L, Xu, S., & Wang, S. (2006). A new method for crude oil price forecasting based on support vector machines. In V. N. Alexandrov, van G. D. Albada, P. M. A. Sloot, J. Dongarra (Eds.), Computational Science – ICCS 2006. Lecture Notes in Computer Science (Vol. 3994, p. 444–451). Berlin: Springer Heidelberg.

[Google Scholar] [CrossRef]

3. Tang, L., & Hammoudeh, S. (2002). An empirical exploration of the world oil price under the target zone model. Fuel And Energy Abstracts, 24(6), 577–596.

[Google Scholar] [CrossRef]

4. Radchenko, S. (2005). Oil price volatility and the asymmetric response of gasoline prices to oil price increases and decreases. Energy economics, 27(5), 708–730.

[Google Scholar] [CrossRef]

5. Pereboichuk, B. (2013). Modeling of Crude Oil Prices With a Special Emphasis on Macroeconomic Factors (Doctoral thesis). Retrieved from http://studenttheses.cbs.dk/bitstream/handle/10417/4420/bogdana_pereboichuk.pdf?sequence

6. Kilian, L., & Murphy, D. P. (2014). The role of inventories and speculative trading in the global market for crude oil. Journal of Applied Econometrics, 29(3), 454–478.

[Google Scholar] [CrossRef]

7. Kaufmann, R. K., Bradford, A., Belanger. L. H., Mclaughlin. J. P., & Miki, Y. (2008). Determinants of OPEC production: Implications for OPEC behavior. Energy Economics, 30(2), 333–351.

[Google Scholar] [CrossRef]

8. Kaufmann, R. K. (2011). The role of market fundamentals and speculation in recent price changes for crude oil. Energy Policy, 39(1), 105–115.

[Google Scholar] [CrossRef]

9. Davig, B. T, Nie, J., & Smith, A. L. (2015). Evaluating a Year of Oil Price Volatility. Retrieved from https://www.kansascityfed.org/~/media/files/publicat/econrev/econrevarchive/2015/3q15davigetal.pdf

[Google Scholar]

10. Bopp, A. E., & Lady, G. M. (1991). A comparison of petroleum futures versus spot prices as predictors of prices in the future. Energy Economics, 13(4), 274–282.

[Google Scholar] [CrossRef]

11. Chatfield, C. (2014). The analysis of time series: an introduction (6th ed.). Ontario: Hoboken CRC Press.

[Google Scholar]

12. Teo, T. T., Logenthiran, T., & Woo, W. L. (2016, November). Forecasting of photovoltaic power using extreme learning machine. In 2016 IEEE Region 10 Conference (TENCON), Singapore, 2016 (p. 455–458).

[Google Scholar] [CrossRef]

13. Li, H., Pan, Y., & Zhou, Q. (2015). Filter design for interval type-2 fuzzy systems with D stability constraints under a unified frame. IEEE Transactions on Fuzzy Systems, 23(3), 719–725.

[Google Scholar] [CrossRef]

14. Farhadi, H., AmirHaeri, M., & Khansari, M. (2015). Alert correlation and prediction using data mining and HMM. The ISC International Journal of Information Security, 3(2), 77–101.

[Google Scholar] [CrossRef]

15. Wilson, A. D., & Bobick, A. F. (1999). Parametric hidden Markov models for gesture recognition. IEEE transactions on pattern analysis and machine intelligence, 21(9), 884–900.

[Google Scholar] [CrossRef]


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Copyright (c) 2017 Nuhu Isah, Abdul Talib Bon

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