Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price

Kaijian He, Rui Zha, Jun Wu and Kin Keung Lai
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Kaijian He: School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China
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

Sustainability, 2016, vol. 8, issue 4, 1-11

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.

Keywords: empirical mode decomposition (EMD); multivariate EMD analysis; crude oil price forecasting; time delay embedding; multiscale analysis; ARMA model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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