Wavelet Entropy Based Analysis and Forecasting of Crude Oil Price Dynamics

For the modeling of complex and nonlinear crude oil price dynamics and movement, wavelet analysis can decompose the time series and produce multiple economically meaningful decomposition structures based on different assumptions of wavelet families and decomposition scale. However, the determination of the optimal model specification will critically affect the forecasting accuracy. In this paper, we propose a new wavelet entropy based approach to identify the optimal model specification and construct the effective wavelet entropy based forecasting models. The wavelet entropy algorithm is introduced to determine the optimal wavelet families and decomposition scale, that will produce the improved forecasting performance. Empirical studies conducted in the crude oil markets show that the proposed algorithm outperforms the benchmark model, in terms of conventional performance evaluation criteria for the model forecasting accuracy.

[1]  Rania Jammazi,et al.  A wavelet-based nonlinear ARDL model for assessing the exchange rate pass-through to crude oil prices , 2015 .

[2]  Jose Alvarez-Ramirez,et al.  Asymmetric long-term autocorrelations in crude oil markets , 2015 .

[3]  Michael L. Mussa.  Global Economic Prospects , 2002 .

[4]  K. Lai,et al.  Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm , 2008 .

[5]  Yan-fang Sang,et al.  Discrete Wavelet Entropy Aided Detection of Abrupt Change: A Case Study in the Haihe River Basin, China , 2012, Entropy.

[6]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[7]  J. Cuaresma,et al.  Modelling and Forecasting Oil Prices: The Role of Asymmetric Cycles , 2009 .

[8]  Enrico Scalas,et al.  Dynamics of avalanche activities in financial markets , 2007 .

[9]  Todd E. Clark,et al.  Approximately Normal Tests for Equal Predictive Accuracy in Nested Models , 2005 .

[10]  Ana Margarida Monteiro,et al.  Market Efficiency, Roughness and Long Memory in PSI20 Index Returns: Wavelet and Entropy Analysis , 2014, Entropy.

[11]  Ronald R. Coifman,et al.  Entropy-based algorithms for best basis selection , 1992, IEEE Trans. Inf. Theory.

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

[13]  M. Hinich.  Testing for Gaussianity and Linearity of a Stationary Time Series. , 1982 .

[14]  Xiao Hu,et al.  Improved wavelet entropy calculation with window functions and its preliminary application to study intracranial pressure , 2013, Comput. Biol. Medicine.

[15]  Alejandra Figliola,et al.  Time-frequency analysis of electroencephalogram series. III. Wavelet packets and information cost function , 1998 .

[16]  J. Álvarez-Ramírez,et al.  US stock market efficiency over weekly, monthly, quarterly and yearly time scales , 2014 .

[17]  Yi Yin,et al.  Weighted multiscale permutation entropy of financial time series , 2014 .

[18]  Sunghee Choi,et al.  Prediction of movement direction in crude oil prices based on semi-supervised learning , 2013, Decis. Support Syst..

[19]  L. Kilian,et al.  What Do We Learn from the Price of Crude Oil Futures? , 2007 .

[20]  J. M. Bates,et al.  The Combination of Forecasts , 1969 .

[21]  C. Li,et al.  A Novel Hybrid Forecasting Method Using GRNN Combined With Wavelet Transform and a GARCH Model , 2015 .

[22]  Robert M. Whaples,et al.  Routledge Handbook of Major Events in Economic History , 2013 .

[23]  Fenghua Wen,et al.  Study on the Fractal and Chaotic Features of the Shanghai Composite Index , 2012 .

[24]  Luiz Fernando Loureiro Legey,et al.  Forecasting oil price trends using wavelets and hidden Markov models , 2010 .

[25]  C. Aloui,et al.  Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling , 2012 .

[26]  A. Sakurai,et al.  Crude Oil Spot Price Forecasting Based on Multiple Crude Oil Markets and Timeframes , 2014 .

[27]  Aviral Kumar Tiwari,et al.  Analyzing time–frequency relationship between oil price and exchange rate in Pakistan through wavelets , 2015 .

[28]  Forecasting Crude Oil Price Movements with Oil‐Sensitive Stocks , 2013 .

[29]  Todd E. Clark,et al.  Using Out-of-Sample Mean Squared Prediction Errors to Test the Martingale Difference Hypothesis , 2004 .

[30]  David J. Parsons,et al.  An improved wavelet–ARIMA approach for forecasting metal prices , 2014 .

[31]  Melike Bildirici,et al.  Forecasting oil prices: Smooth transition and neural network augmented GARCH family models , 2013 .

[32]  Willi Semmler,et al.  Interest rate spreads and output: A time scale decomposition analysis using wavelets , 2014, Comput. Stat. Data Anal..

[33]  R. Gencay,et al.  Multiscale systematic risk , 2005 .

[34]  R. Gencay,et al.  An Introduction to Wavelets and Other Filtering Methods in Finance and Economics , 2001 .

[35]  J. Barkoulas,et al.  A metric and topological analysis of determinism in the crude oil spot market , 2012 .

[36]  Jun Wang,et al.  Quantifying complexity of financial short-term time series by composite multiscale entropy measure , 2015, Commun. Nonlinear Sci. Numer. Simul..

[37]  S. Samantaray,et al.  Wavelet Singular Entropy-Based Islanding Detection in Distributed Generation , 2013, IEEE Transactions on Power Delivery.

[38]  Ling Tang,et al.  A novel seasonal decomposition based least squares support vector regression ensemble learning appro , 2011 .

[39]  Alireza Talaei,et al.  Predicting oil price movements: A dynamic Artificial Neural Network approach , 2014 .

[40]  Stelios D. Bekiros,et al.  Oil Price Forecastability and Economic Uncertainty , 2015, SSRN Electronic Journal.

[41]  Theodore Panagiotidis.  Testing the assumption of Linearity , 2002 .

[42]  Jose Alvarez-Ramirez,et al.  Efficiency of crude oil markets: Evidences from informational entropy analysis , 2012 .

[43]  Bing Zhang,et al.  Testing the evolution of crude oil market efficiency: Data have the conn , 2014 .

[44]  David A. Hsieh,et al.  Implications of Nonlinear Dynamics for Financial Risk Management , 1993, Journal of Financial and Quantitative Analysis.

[45]  J. Álvarez-Ramírez,et al.  Multiscale entropy analysis of crude oil price dynamics , 2011 .

[46]  B. LeBaron,et al.  Nonlinear Dynamics, Chaos, and Instability: Statistical Theory and Economic Evidence , 1991 .

[47]  Ilona Weinreich,et al.  Wavelet-based prediction of oil prices , 2005 .

[48]  Faridul Islam,et al.  Time–frequency relationship between share prices and exchange rates in India: Evidence from continuous wavelets , 2015 .