Oil Price Predictions Based on Nonlinear Smooth Transition Models, Using Genetic Algorithm Optimization

Asgharpur, Hossein; Vafamand, Ali

doi:10.22051/edp.2015.73

Oil Price Predictions Based on Nonlinear Smooth Transition Models, Using Genetic Algorithm Optimization

Authors

Hossein Asgharpur ¹
Ali Vafamand ²

¹ Associate Professor, Tabriz University, Faculty of Economics

² Ph.D. student of economics, Tabriz University, Faculty of Economics

10.22051/edp.2015.73

Abstract

The price of oil plays an important role in the global economy and is an important factor influencing the government and
commercial sectors. Because of increasing importance of oil in financial markets, oil price predictions have always been an important subject for the researchers in Economics, and other economic agents. This paper tries to study the behavior of crude oil prices based on Smooth Transition Autoregressive models used on monthly crude oil prices data. We show
that simulation results using STAR models estimated by Genetic Algorithm method, outperforms linear time series models, such as ARIMA, for out of sample predictions based on RMSE and MAE and DA criteria.

Keywords

References

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- Shabri, A. & Ruhaidah, S. (2014). Daily Crude Oil Price Forecasting Using Hybridizing Wavelet and Artificial Neural Network Model. Mathematical Problems in Engineering 2014, pp.1-10.

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- Sompui, M. & Wullapa, W. (2014). Prediction Model for Crude Oil Price Using Artificial Neural Networks. Applied Mathematical Sciences, Vol.8, No.80, pp. 3953 - 3965

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Volume 2, Issue 3 - Serial Number 4
October 2014
Pages 69-94

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Oil Price Predictions Based on Nonlinear Smooth Transition Models, Using Genetic Algorithm Optimization

References

- Abosedra, S. (2005). Futures versus Univariate Forecast of Crude Oil Prices. OPEC Review, Vol. 29, No. 4, pp. 231–241.

- Ahmed, R. A. & Bin, S. A. (2013). Fitting GARCH Models to Crude Oil Spot Price Data, Life Science Journal, Vol.10, No.4, pp. 654-661

- Ahmed, R. A. & Bin, S. A. (2014). Daily Crude Oil Price Forecasting Model Using ARIMA, Generalized Autoregressive Conditional Heteroscedastic and Support Vector Machines, American Journal of Applied Sciences, Vol.11, No.3, pp. 425-432.

- Alquist, R. Kilian, L. & Vigfusson, R. J. (2011). Forecasting the Price of Oil. International Finance Discussion Papers1022, Board of Governors of the Federal Reserve System (U.S.).

- Bopp, A. E. & Lady, G. M. (1991). A Comparison of Petroleum Futures versus Spot Prices as Predictors of Prices in the Future. Energy Economics, Vol.13, No.4, pp. 274–282.

- Chernenko, S. Schwarz, K. & Wright. J. H. (2004). The Information Content of Forward and Futures Prices: Market Expectations and the Price of Risk. FRB International Finance Discussion Paper 808.

- Day, T. E. & Lewis. C.M. (1993). Forecasting Futures Markets Volatility. the Journal of Derivatives, Vol.1, No.2, pp. 33-50.

- Duffie, D. & Gray. S. (1995). Volatility in Energy Prices, Managing Energy Price Risk. Risk Publications, London, pp. 39-55.

- Lalonde, R. Zhu, Z. & Demers, F. (2003). Forecasting and Analyzing World Commodity Prices. Bank of Canada, Working Paper, 2003–24.

- Luukkonen, R. & Terasvirta, T. (1991).Testing Linearity of Econometric Time Series Against Cyclical Asymmetry. Annales d’economie et de Statistique, 20/21, pp.125–142.

- Marzo, M. & Zagaglia, P. (2007). Volatility Forecasting for Crude Oil Futures. Research Papers in Economics,2007:9, Stockholm University, Department of Economics.

- Mehrara, M. Moeini, A. Ahrari, M. & Erfanifard, A. (2009). Investigating the Efficiency in Oil Futures Market Based on GMDH Approach. Journal of Expert Systems with Applications, Vol.36, Issue 4, pp. 7337-8712.

- Mitchell, M. (1996). An Introduction to Genetic Algorithms. MIT Press, Cambridge, MA.

- Morana, C. (2001). A Semiparametric Approach to Short-term Oil Price Forecasting. Energy Economics, Vol.23, Issue3, pp. 325–338.

- Musaddiq, T. (2012). Modeling and Forecasting the Volatility of Oil Futures Using the ARCH Family Models. The Lahore Journal of Business,Vol.1, No.1, pp. 79–108.

- Schwartz, E. & Smith, J. E. (2000). Short-term Variations and Long-term Dynamics in Commodity Prices. Management Science, Vol. 46, No.7, pp. 893–911.

- Shabri, A. & Ruhaidah, S. (2014). Daily Crude Oil Price Forecasting Using Hybridizing Wavelet and Artificial Neural Network Model. Mathematical Problems in Engineering 2014, pp.1-10.

- Shambora, W. E. & Rossiter, I. (2007). Are There Expoitable Inefficiencies in the Futures Market for Oil? Energy Economics, Vol.29, Issue1, pp.18-27.

- Sompui, M. & Wullapa, W. (2014). Prediction Model for Crude Oil Price Using Artificial Neural Networks. Applied Mathematical Sciences, Vol.8, No.80, pp. 3953 - 3965

- Terasvirta, T.)1994(. Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association.Vol.89, Issue 425, pp.208–218.

- Xiong, T. Bao, Y. & Hu, Z. (2013). Beyond One-Step-Ahead Forecasting: Evaluation of Alternative Multi-Step-Ahead Forecasting Models for Crude Oil Prices. Energy Economics, Vol.40, Supplement 1, pp.405–415.

- Ye, M. J. Zyren & Shore, J. (2005). A Monthly Crude Oil Spot Price Forecasting Model Using Relative Inventories. Internat-ional Journal of Forecasting, Vol. 21, No.3, pp. 491–501.

Volume 2, Issue 3 - Serial Number 4
October 2014
Pages 69-94

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Oil Price Predictions Based on Nonlinear Smooth Transition Models, Using Genetic Algorithm Optimization

References

- Abosedra, S. (2005). Futures versus Univariate Forecast of Crude Oil Prices. OPEC Review, Vol. 29, No. 4, pp. 231–241.

- Ahmed, R. A. & Bin, S. A. (2013). Fitting GARCH Models to Crude Oil Spot Price Data, Life Science Journal, Vol.10, No.4, pp. 654-661

- Ahmed, R. A. & Bin, S. A. (2014). Daily Crude Oil Price Forecasting Model Using ARIMA, Generalized Autoregressive Conditional Heteroscedastic and Support Vector Machines, American Journal of Applied Sciences, Vol.11, No.3, pp. 425-432.

- Alquist, R. Kilian, L. & Vigfusson, R. J. (2011). Forecasting the Price of Oil. International Finance Discussion Papers1022, Board of Governors of the Federal Reserve System (U.S.).

- Bopp, A. E. & Lady, G. M. (1991). A Comparison of Petroleum Futures versus Spot Prices as Predictors of Prices in the Future. Energy Economics, Vol.13, No.4, pp. 274–282.

- Chernenko, S. Schwarz, K. & Wright. J. H. (2004). The Information Content of Forward and Futures Prices: Market Expectations and the Price of Risk. FRB International Finance Discussion Paper 808.

- Day, T. E. & Lewis. C.M. (1993). Forecasting Futures Markets Volatility. the Journal of Derivatives, Vol.1, No.2, pp. 33-50.

- Duffie, D. & Gray. S. (1995). Volatility in Energy Prices, Managing Energy Price Risk. Risk Publications, London, pp. 39-55.

- Lalonde, R. Zhu, Z. & Demers, F. (2003). Forecasting and Analyzing World Commodity Prices. Bank of Canada, Working Paper, 2003–24.

- Luukkonen, R. & Terasvirta, T. (1991).Testing Linearity of Econometric Time Series Against Cyclical Asymmetry. Annales d’economie et de Statistique, 20/21, pp.125–142.

- Marzo, M. & Zagaglia, P. (2007). Volatility Forecasting for Crude Oil Futures. Research Papers in Economics,2007:9, Stockholm University, Department of Economics.

- Mehrara, M. Moeini, A. Ahrari, M. & Erfanifard, A. (2009). Investigating the Efficiency in Oil Futures Market Based on GMDH Approach. Journal of Expert Systems with Applications, Vol.36, Issue 4, pp. 7337-8712.

- Mitchell, M. (1996). An Introduction to Genetic Algorithms. MIT Press, Cambridge, MA.

- Morana, C. (2001). A Semiparametric Approach to Short-term Oil Price Forecasting. Energy Economics, Vol.23, Issue3, pp. 325–338.

- Musaddiq, T. (2012). Modeling and Forecasting the Volatility of Oil Futures Using the ARCH Family Models. The Lahore Journal of Business,Vol.1, No.1, pp. 79–108.

- Schwartz, E. & Smith, J. E. (2000). Short-term Variations and Long-term Dynamics in Commodity Prices. Management Science, Vol. 46, No.7, pp. 893–911.

- Shabri, A. & Ruhaidah, S. (2014). Daily Crude Oil Price Forecasting Using Hybridizing Wavelet and Artificial Neural Network Model. Mathematical Problems in Engineering 2014, pp.1-10.

- Shambora, W. E. & Rossiter, I. (2007). Are There Expoitable Inefficiencies in the Futures Market for Oil? Energy Economics, Vol.29, Issue1, pp.18-27.

- Sompui, M. & Wullapa, W. (2014). Prediction Model for Crude Oil Price Using Artificial Neural Networks. Applied Mathematical Sciences, Vol.8, No.80, pp. 3953 - 3965

- Terasvirta, T.)1994(. Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association.Vol.89, Issue 425, pp.208–218.

- Xiong, T. Bao, Y. & Hu, Z. (2013). Beyond One-Step-Ahead Forecasting: Evaluation of Alternative Multi-Step-Ahead Forecasting Models for Crude Oil Prices. Energy Economics, Vol.40, Supplement 1, pp.405–415.

- Ye, M. J. Zyren & Shore, J. (2005). A Monthly Crude Oil Spot Price Forecasting Model Using Relative Inventories. Internat-ional Journal of Forecasting, Vol. 21, No.3, pp. 491–501.

Volume 2, Issue 3 - Serial Number 4October 2014Pages 69-94

Files

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Volume 2, Issue 3 - Serial Number 4
October 2014
Pages 69-94