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A new approach for crude oil price analysis based on Empirical Mode Decomposition

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  • Zhang, Xun
  • Lai, K.K.
  • Wang, Shou-Yang
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    Abstract

    The importance of understanding the underlying characteristics of international crude oil price movements attracts much attention from academic researchers and business practitioners. Due to the intrinsic complexity of the oil market, however, most of them fail to produce consistently good results. Empirical Mode Decomposition (EMD), recently proposed by Huang et al., appears to be a novel data analysis method for nonlinear and non-stationary time series. By decomposing a time series into a small number of independent and concretely implicational intrinsic modes based on scale separation, EMD explains the generation of time series data from a novel perspective. Ensemble EMD (EEMD) is a substantial improvement of EMD which can better separate the scales naturally by adding white noise series to the original time series and then treating the ensemble averages as the true intrinsic modes. In this paper, we extend EEMD to crude oil price analysis. First, three crude oil price series with different time ranges and frequencies are decomposed into several independent intrinsic modes, from high to low frequency. Second, the intrinsic modes are composed into a fluctuating process, a slowly varying part and a trend based on fine-to-coarse reconstruction. The economic meanings of the three components are identified as short term fluctuations caused by normal supply-demand disequilibrium or some other market activities, the effect of a shock of a significant event, and a long term trend. Finally, the EEMD is shown to be a vital technique for crude oil price analysis.

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    Bibliographic Info

    Article provided by Elsevier in its journal Energy Economics.

    Volume (Year): 30 (2008)
    Issue (Month): 3 (May)
    Pages: 905-918

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    Handle: RePEc:eee:eneeco:v:30:y:2008:i:3:p:905-918

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    Web page: http://www.elsevier.com/locate/eneco

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    References

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    1. Pindyck, Robert S., 1998. "The long-run evolution of energy prices," Working papers WP 4044-98., Massachusetts Institute of Technology (MIT), Sloan School of Management.
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    9. Dees, Stephane & Karadeloglou, Pavlos & Kaufmann, Robert K. & Sanchez, Marcelo, 2007. "Modelling the world oil market: Assessment of a quarterly econometric model," Energy Policy, Elsevier, vol. 35(1), pages 178-191, January.
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    Cited by:
    1. Bangzhu Zhu, 2012. "A Novel Multiscale Ensemble Carbon Price Prediction Model Integrating Empirical Mode Decomposition, Genetic Algorithm and Artificial Neural Network," Energies, MDPI, Open Access Journal, vol. 5(2), pages 355-370, February.
    2. He, Kaijian & Lai, Kin Keung & Yen, Jerome, 2011. "Value-at-risk estimation of crude oil price using MCA based transient risk modeling approach," Energy Economics, Elsevier, vol. 33(5), pages 903-911, September.
    3. Li, Muyi & Huang, Yongxiang, 2014. "Hilbert–Huang Transform based multifractal analysis of China stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 406(C), pages 222-229.
    4. Zhang, Xun & Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2009. "Estimating the impact of extreme events on crude oil price: An EMD-based event analysis method," Energy Economics, Elsevier, vol. 31(5), pages 768-778, September.
    5. Oladosu, Gbadebo, 2009. "Identifying the oil price-macroeconomy relationship: An empirical mode decomposition analysis of US data," Energy Policy, Elsevier, vol. 37(12), pages 5417-5426, December.

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