Forecasting Price Direction, Hedging and Spread Options in Oil Volatility
International Journal of Economic Behavior and Organization
Volume 5, Issue 6, December 2017, Pages: 114-123
Received: Apr. 5, 2017; Accepted: Oct. 8, 2017; Published: Nov. 8, 2017
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Authors
Eboka Andrew Okonji, Department of Computer Education, Federal College of Education Technical, Asaba, Nigeria
Yerokun Oluwatoyin Mary, Department of Computer Education, Federal College of Education Technical, Asaba, Nigeria
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Abstract
The energy market aims to manage risks associated with prices and volatility of the asset. It is a capital intensive market, rippled with a range of chaotic, complex and dynamic interaction among its supply and demand derivatives. Models help users forecast such interactions, to provide investors with empirical evidence of the price direction. Evolutionary modeling is an art, whose science seeks to analyze input data and yield an optimal, complete solution for which conventional methods yield a corresponding, non-cost effective solution. Its solutions are tractable, robust and low-cost with a tolerance of ambiguity, uncertainty and noise as applied to its input. Our study aims to predict the OPEC Oil market with data collected over the period.
Keywords
Energy, OPEC, Stochastic, Evolutionary Model, Price Direction, Volatility
To cite this article
Eboka Andrew Okonji, Yerokun Oluwatoyin Mary, Forecasting Price Direction, Hedging and Spread Options in Oil Volatility, International Journal of Economic Behavior and Organization. Vol. 5, No. 6, 2017, pp. 114-123. doi: 10.11648/j.ijebo.20170506.11
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Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
References
[1]
Abosedra, S and Baghestani, H., (2004). On predictive accuracy of futures prices, Energy Policy, pp. 1389-1393.
[2]
Abraham, A., (2005). Handbook of Measuring System Design, John Wiley and Sons Ltd, ISBN: 0-470-02143-8, pp. 901–918.
[3]
Azoff, M., (1994). Neural network time series forecasting of financial markets, John Wiley & Sons, Chichester.
[4]
Behmiri, N. B and Manso, J. R. P., (2013). Crude oil forecasting techniques: a comprehensive review of literature, Alternative Investment Analyst Review in Social Science Review Network, doi: 10.2139/ssrn.2275428, 30-48
[5]
Beven, K. J and Binley, N. J., (1992). Knowledge driven models and computations, Wiley and Sons, Chichester, ISBN: 0-465-08245-3.
[6]
Bishop, C. M., (1995). Neural networks for pattern recognition, Oxford University Press, ISBN: 0-342-45230, pp.253-294.
[7]
Bopp, E and Sitzer, S., (1987). Are petroleum futures prices good predictors of cash value?, Journal of Futures Market, 705-719, 1987.
[8]
Branke, J., (2001). Evolutionary Optimization in Dynamic Environments, Kluwer.
[9]
Brooks, C., (2002). Introductory econometrics for finance, Cambridge University press, Cambridge.
[10]
Brooks, C., Rew, G., Alistair, S and Ritson, S., (2001). A trading strategy based on lead-lag relationship between the spot index and futures contracts for FTSE 100, International Journal of Forecasting, 17: 31-44.
[11]
Campolo, M., Andreussi, P and Soldati, A., (1999). Flood forecasting with a neural network, Water Resources, 35(4): 1191.
[12]
Caudill M., (1987). Neural Networks Primer, AI Expert December, pp. 46-52.
[13]
Chan, K., (1992). A further analysis of the lead-lag relationship between the cash market and stock index futures market, The Review of Financial Studies, 7(6): 123-152.
[14]
Chakroborty, A. L., (2010). Introductory artificial intelligence, Lecture note on Computer Science, Massachuset Institute of Technology, [online]: www.mit.courseware
[15]
Cherry, H. (2007). Financial Economics, 1st Ed. Actuarial Study Materials, NY: 3217 Wynsum Ave., Merrick.
[16]
Coello, C., Pulido, G and Lechuga, M., (2004). Handling multiple objectives with particle swarm optimization, In Proceedings of Evolutionary Computing, Vol. 8, pp 256–279.
[17]
Coppola, A., (2007). Forecasting oil price movements: exploiting the information in futures market, 34, http://papers.ssrn.com/paper.taf?paper_id=967408
[18]
Dawson, C and Wilby, R., (2001a). Comparison of neural networks in river flow forecasting, J. of Hydrology and Earth Science, SRef-ID: 1607-7938/hess/2001-3-529.
[19]
Dawson, C and Wilby, R., (2001b). Hydrology modeling using neural network, Journal of Programming in Physics and Geography, 25: 80–108.
[20]
Dontwi, I. K., Dedu, V. K and Davis, R., (2010). Application of options in hedging of crude oil price risk, American Journal of Social and Management Sciences, ISSN: 2156-1540, doi: 10.5251/ajsms.2010.1.1.67.74.
[21]
Fausett L., (1994). Fundamentals of Neural Networks, New Jersey: Prentice Hall, pp.240.
[22]
French, M. W., (2005). Why and When do Spot Prices of Crude Oil Revert to Futures Price Levels?, Finance and Economics Discussion Series Divisions of Research and Statistics and Monetary Affairs, Federal Reserve Board, Washington, D. C.
[23]
Gabillon, J., (1991). Terms of oil future prices, Oxford institute of Energy Studies, ISBN: 0-948061-59-6
[24]
Heppner, H and Grenander, U., (1990). Stochastic non-linear model for coordinated bird flocks, In Krasner, S (Ed.), The ubiquity of chaos (pp.233–238). Washington: AAAS.
[25]
Karunanithi, N., Grenney, W., Whitley, D and Bovee, K., (1994). Neural network for river prediction”, Journal of Computing in Civil Engineering, 8(2), pp.201–220.
[26]
Kulkarni, S and 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): 38–49.
[27]
Labonte, M., (2004). The effect of oil shocks on the economy: A review of the empirical evidence, RL31608.
[28]
Laurenti, M and Fernandes, J. M. M., (2012). Pricing crude oil calendar spread option, Master’s Degree Thesis, Department of Finance, Copenhagen Business School.
[29]
Mandic, D and Chambers, J., (2001). Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability, Wiley and Sons: New York, pp56-90.
[30]
McNeils, D., (2005). Neural networks in finance gaining predictive edge in the market, Elsevier Academic Press, Massachusetts.
[31]
Minns, A., (1998). Artificial neural networks as sub-symbolic process descriptors, published PhD Thesis, Balkema, Rotterdam, Netherlands.
[32]
Moshiri, S and Foroutan, F., (2005). Forecasting nonlinear oil futures prices, The Energy Journal, 27: 81-95.
[33]
Ojugo, A. A, (2012). A hybrid neural networks trained gravitational search algorithm for rainfall runoff modeling, PhD Thesis, CS Department: Ebonyi State University Abakiliki, Nigeria.
[34]
Ojugo, A. A, (2016). Spread options pricing and hedging for Oil market: a case of OPEC, Lecture Series of the Federal University of Petroleum Resources Effurun, Nigeria.
[35]
Ojugo, A. A., and Yoro, R., (2013a). Computational intelligence in stochastic solution for Toroidal Queen, Progress in Intelligence Computing and Applications, 2(1), pp 46–56.
[36]
Ojugo, A. A and Ofualagba, G. O., (2016). Repositioning FUPRE Petroleum Varsity to tackle challenges in the oil and gas sector of Nigeria, Guardian Newspaper.
[37]
Ojugo, A. A., Emudianughe, J., Yoro, R. E., Okonta, E and Eboka, A. O., (2013b). Hybrid neural network gravitational search algorithm for rainfall runoff modeling, Progress in Intelligence Computing and Application, 2(1), doi: 10.4156/pica.vol2.issue1.2, 22–33.
[38]
Ojugo. A. A., A. O. Eboka., R. E. Yoro., M. O. Yerokun., F. N. Efozia., (2015). Hybrid model for early diabetes diagnosis, Mathematics and Computers in Science and Industry (A Mathematics and Computers in Science and Engineering Series), 50: 176-182, ISBN: 978-1-61804-327-6, ISSN: 2227-4588.
[39]
Ojugo. A. A., A. O. Eboka., R. E. Yoro., M. O. Yerokun., F. N. Efozia., (2015). Framework design for statistical fraud detection, Mathematics and Computers in Science and Industry (Mathematics and Computers in Science and Engineering Series), 50: 176-182, ISBN: 978-1-61804-327-6, ISSN: 2227-4588.
[40]
A. A. Ojugo., I. J. B. Iyawa., F. O. Aghware., M. O. Yerokun., E. Ugboh., (2015). Comparative study of timetable constraint satisfaction problem, Latest trends in Circuits, Systems, Signal Processing and Automatic Control, 34: 176-182, ISBN: 978-960-474-374-2, ISSN: 1790-5117.
[41]
Perez, M and Marwala, T., (2011). Stochastic optimization approaches for solving Sudoku, IEEE Transaction on Evol. Comp., pp.256–279.
[42]
Rajurkar, M. P., Kothyari, U and Chaube, U., (2004). Modeling of daily rainfall-runoff relationship with artificial neural network, Journal of Hydrology, Vol. 28, 96–113.
[43]
Refenes, A. A., (1995). Neural networks in the capital markets, John Wiley and Sons, New York.
[44]
Reggiani, P. and Rientjes, T., (2005). Flux parameterization in the representative elementary watershed approach: Application to basin Water Resources, 41(4), pp 18-27.
[45]
Reynolds, R., (1994). Introduction to cultural algorithms, Transaction on Evolutionary Programming (IEEE), pp.131-139.
[46]
Rouah, F. D and Vainberg, G., (2007). Option pricing and volatility using Excel VBA, John Wiley and Sons, NY, ISBN: 978-0-471-79464-6.
[47]
Sharma, N., (1998). Forecasting oil price volatility, Masters of Art Thesis in Economics, Virginia Polytechnic Institute and State University.
[48]
Silvapulle, P and Mossa, A., (1999). The relation between spot and future prices: Evidence from the crude oil market, The Journal of Futures Markets, 19(2), 175-193.
[49]
Smith, M., (1993). Neural networks for statistical modeling, Van Nostrand Reinholo, New York.
[50]
The International Energy Agency (2009) Analysis of the impact of high oil prices on the global economy, Annual Report.
[51]
Ursem, R., Krink, T., Jensen, M. and Michalewicz, Z., (2002). Analysis and modeling of controls in dynamic systems. IEEE Transaction on Memetic Systems and Evolutionary Computing, 6(4): 378-389.
[52]
Vanstone, B., (2005). Trading in Australian stock market using artificial neural networks, McGraw Hill publication.
[53]
Verleger, P. K. (1993). Adjusting to Volatile Energy Prices, Institute for International Economics, Washington DC.
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