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2017 | 3 | 8 | 1007-1012
Tytuł artykułu

Application of Markov Model in Crude Oil Price Forecasting

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Słowa kluczowe
Czasopismo
Rocznik
Tom
3
Numer
8
Strony
1007-1012
Opis fizyczny
Daty
wydano
2017-08-16
Twórcy
autor
  • Universiti Tun Hussein Onn Malaysia
  • Universiti Tun Hussein Onn Malaysia
Bibliografia
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  • 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. doi: 10.1007/11758549_63
  • 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. doi: 10.1016/S0140-6701(03)81648-X
  • 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. doi: 10.1016/j.eneco.2005.06.001
  • 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
  • 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. doi: 10.1002/jae.2322
  • 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. doi: 10.1016/j.eneco.2007.04.003
  • Kaufmann, R. K. (2011). The role of market fundamentals and speculation in recent price changes for crude oil. Energy Policy, 39(1), 105–115. doi: 10.1016/j.enpol.2010.09.018
  • 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
  • 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. doi: 10.1016/0140-9883(91)90007-m
  • Chatfield, C. (2014). The analysis of time series: an introduction (6th ed.). Ontario: Hoboken CRC Press.
  • 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). doi: 10.1109/TENCON.2016.7848040
  • 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. doi: 10.1109/tfuzz.2014.2315658
  • 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. doi: 10.22042/isecure.2015.3.2.3
  • 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. doi: 10.1109/34.790429
Typ dokumentu
Bibliografia
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