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Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price

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  • Kaijian He

    (School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China
    Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Kowloon, Hong Kong)

  • Rui Zha

    (School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China)

  • Jun Wu

    (School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China)

  • Kin Keung Lai

    (International Business School, Shaanxi Normal University, Xi’an 710119, China
    Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong)

Abstract

Recent empirical studies reveal evidence of the co-existence of heterogeneous data characteristics distinguishable by time scale in the movement crude oil prices. In this paper we propose a new multivariate Empirical Mode Decomposition (EMD)-based model to take advantage of these heterogeneous characteristics of the price movement and model them in the crude oil markets. Empirical studies in benchmark crude oil markets confirm that more diverse heterogeneous data characteristics can be revealed and modeled in the projected time delayed domain. The proposed model demonstrates the superior performance compared to the benchmark models.

Suggested Citation

  • Kaijian He & Rui Zha & Jun Wu & Kin Keung Lai, 2016. "Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price," Sustainability, MDPI, vol. 8(4), pages 1-11, April.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:4:p:387-:d:68672
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