MODELING BRENT OIL PRICE WITH MARKOV CHAIN PROCESS OF THE FUZZY STATES
Purpose - The rapid change of crude oil price in the international market has attacted several investors into examining price fluctuations. The estimation regarding to the exact monthly price of the brent oil has always been a diffucult task in the business sector. Methodology - In this study, the directions of the monthly Brent oil prices from January 2003 to January 2017are analyzed using the Markov Chains of Fuzzy States technique. In the first instance, the data are classified into twenty-one fuzzy states, and then calculated the probability transition matrix of the fuzzy states for the given period. Findings- The directions of the monthly Brent oil prices are analyzed with transition matrix. Next the steady condition of the Brent oil return is obtained. These results give valuable information to decision makers regarding the investment opportunities of Brent oil for the short and long term marketing strategies.Conclusion- In crucial months, when a monthly return increases or decreases significantly, the proceeding month’s expected return also increase or decreases significantly. The proposed model can be used to estimate short term returns (one day) and also employing several fuzzy sets may give more investment opportunities.
___
- Avrachenkov K.E., Sanchez E., (2000). Fuzzy Markoc Chains. IPMU, 1851-1856.
- Gileva, T., (2010). Econometrics of crude oil markets. Universite Paris 1. http://www.ebooks-for-all.com eBookEdition:2010.
- Guo, X., Li, D. & Zhang, A., (2012). Improved support vector machine oil price forecast model based on genetic algorithm optimization parameters. AASRI Procedia 1, 525–530.
- Investing.com, (2018). www.investing.com. Retrieved 5 February 2018, from https://www.investing.com.
- Kıral E., Uzun, B., (2017). Forecasting Closing Returns of Borsa Istanbul Index with Markov Chain Process of the Fuzzy States. Journal of Economics, Finance and Accounting 4(1), 15-23.
- Kruce, R., Buck- Emden, R., Cordes, R., (1987). Process or Power Considerations: An Application to Fuzzy Markov Chains. Fuzzy Sets and Systems, 289-299.
- Kuranoa, M., Yasuda, M., Jakagami, J., Yoshida, Y., (2006). A Fuzzy Approach to Markov Decision Processes with Unceratin Transition Probabilities. Fuzzy Sets and Systems 157, 2674-2682.
- Narayan, P. K. & Narayan, S., (2007). Modelling oil price volatility. Energy Policy 35(12), 6549–6553.
- Pardo, M.J., Fuente, D., (2010). Fuzzy Markovian Decision Processes: Application to Queueing Systems. Computers and Mathematics with Applications.” 60, 2526-2535.
- Sadorsky, P., (2006). Modeling and forecasting petroleum futures volatility. Energy Economics 28(4), 467–488.
- Uzun, B., Kıral, E., (2017). Application of Markov chains-fuzzy states to gold price. Procedia Computer Science, 120, 365-371.
- Vajargah, B.F., Gharehdaghi, M., (2012). Ergodicity of fuzzy Makov chains based on simulation using Halton sequences. The Journal of Mathematics and Computer Science 4(3), 380-385.
- Xie, W., Yu, L., Xu, S. & Wang, S, (2006). A new method for crude oil price forecasting based on support vector machines. Computational Science–ICCS 2006’. Springer, 444–451.
- Yoshida, Y., (1994). Markov chains with a transition possibility measure and fuzzy dynamic programming. Fuzzy Sets and Systems 66, 3957.
- Zhou, X., Tang, Y., Xie, Y., Li, Y., Zhang, Y., (2013). A Fuzzy Probability- based Markov Chain Model for Electric Power Demand Forecasting of Beijing, China. Energy and Power Engineering, 488-492.
- Yayın Aralığı: Yılda 4 Sayı
- Başlangıç: 2014
- Yayıncı: PressAcademia