A Hybrid Approach for Forecasting of Oil Prices Volatility

This study aims to introduce an ideal model for forecasting crude oil price volatility. For this purpose, the ‘predictability’ hypothesis was tested using the variance ratio test, BDS test and the chaos analysis. Structural analyses were also carried out to identify possible non-linear patterns in this series. On this basis, Lyapunov exponents confirmed that the return series of crude oil price is chaotic. Moreover, according to the findings, the rate of return series has the long memory property rejecting the efficient market hypothesis and affirming the fractal markets hypothesis. The results of the Geweke and Porter-Hudak test verified that both the rate of return and volatility series of crude oil price have the long memory property. Besides, according to both mean square error (MSE) and root mean square error (RMSE) criteria, wavelet-decomposed data improve the performance of the model significantly. Therefore, a hybrid model was introduced based on the long memory property which uses wavelet decomposed data as the most relevant model.

[1]  M. Farzanegan,et al.  The effects of oil price shocks on the Iranian economy , 2009 .

[2]  J. Alvarez,et al.  Crude oil market efficiency and modeling: Insights from the multiscaling autocorrelation pattern , 2010 .

[3]  A. Lo,et al.  Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test , 1987 .

[4]  M. Mehrara,et al.  THE SOURCES OF MACROECONOMIC FLUCTUATIONS IN OIL EXPORTING COUNTRIES: A COMPARATIVE STUDY , 2007 .

[5]  B. LeBaron,et al.  A test for independence based on the correlation dimension , 1996 .

[6]  Mohd Tahir Ismail,et al.  Selecting Wavelet Transforms Model in Forecasting Financial Time Series Data Based on ARIMA Model , 2011 .

[7]  Yu Wei,et al.  Forecasting crude oil market volatility: Further evidence using GARCH-class models , 2010 .

[8]  Rodney C. Wolff,et al.  Forecasting crude oil price (revisited) , 2011 .

[9]  K. Lai,et al.  Crude oil price analysis and forecasting using wavelet decomposed ensemble model , 2012 .

[10]  Chin Wen Cheong,et al.  Modeling and forecasting crude oil markets using ARCH-type models , 2009 .

[11]  Macroeconomic Dynamics in the Oil Exporting Countries: A Panel VAR study , 2011 .

[12]  Seong‐Min Yoon,et al.  Structural changes and volatility transmission in crude oil markets , 2011 .

[13]  Olivier Darné,et al.  The efficiency of the crude oil markets: Evidence from variance ratio tests , 2009 .

[14]  Ali Amiri,et al.  Stock price prediction using a fusion model of wavelet , fuzzy logic and ANN , .

[15]  H. E. Hurst,et al.  Long-Term Storage Capacity of Reservoirs , 1951 .

[16]  Yu Wei,et al.  Can GARCH-class models capture long memory in WTI crude oil markets? , 2011 .

[17]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[18]  Duc Khuong Nguyen,et al.  Time-varying Predictability in Crude Oil Markets: The Case of GCC Countries , 2010 .

[19]  Maxwell L. King,et al.  Editors' introduction: Fractional differencing and long memory processes , 1996 .

[20]  J. Geweke,et al.  THE ESTIMATION AND APPLICATION OF LONG MEMORY TIME SERIES MODELS , 1983 .

[21]  William A. Brock,et al.  A rational route to randomness , 1997 .

[22]  Jose Alvarez-Ramirez,et al.  Short-term predictability of crude oil markets: A detrended fluctuation analysis approach , 2008 .

[23]  Pedro M. Saraiva,et al.  Denoising and Signal-to-Noise Ratio Enhancement: Wavelet Transform and Fourier Transform , 2009 .

[24]  Duc Khuong Nguyen,et al.  Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models , 2012 .

[25]  N. G. Alikhani,et al.  The Long-run and Short-run Effects of Crude Oil Price on Methanol Market in Iran , 2012 .

[26]  H. Lorenz.  Nonlinear Dynamical Economics and Chaotic Motion , 1989 .

[27]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[28]  Afees A. Salisu,et al.  Modelling oil price volatility with structural breaks , 2013 .

[29]  S. Prado.  Free lunch in the oil market: a note on Long Memory , 2011 .

[30]  David A. Swanson,et al.  MAPE-R: a rescaled measure of accuracy for cross-sectional subnational population forecasts , 2011 .

[31]  A. Wolf,et al.  Determining Lyapunov exponents from a time series , 1985 .

[32]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

[33]  Jumat Sulaiman,et al.  Application of Wavelet Method in Stock Exchange Problem , 2011 .

[34]  C. Granger,et al.  AN INTRODUCTION TO LONG‐MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING , 1980 .

[35]  A. Gallant,et al.  Convergence rates and data requirements for Jacobian-based estimates of Lyapunov exponents from data , 1991 .

[36]  C Jessy John,et al.  Analysis of Non-Stationary Time Series using Wavelet Decomposition , 2010 .

[37]  Shawkat Hammoudeh,et al.  Long Memory in Oil and Refined Products Markets , 2009 .

[38]  Minh-Vuong Vo.  Oil and stock market volatility: A multivariate stochastic volatility perspective , 2011 .

[39]  G. Özer,et al.  Chaotic Processes of Common Stock Index Returns: An Empirical Examination on Istanbul Stock Exchange (ISE) Market , 2010 .

[40]  H. Mohammadi,et al.  International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models , 2010 .

[41]  Nese Erbil.  Is Fiscal Policy Procyclical in Developing Oil-Producing Countries? , 2011, SSRN Electronic Journal.