Crude oil price forecasting based on a novel hybrid long memory GARCH-M and wavelet analysis model

Ling Lin, Yong Jiang, Helu Xiao and Zhongbao Zhou

Physica A: Statistical Mechanics and its Applications, 2020, vol. 543, issue C

Abstract: This paper proposes a novel hybrid forecast model to forecast crude oil price on considering the long memory, asymmetric, heavy-tail distribution, nonlinear and non-stationary characteristics of crude oil price. First, we use a signal de-noising method to reduce excessive noise significantly in the crude oil price. Then we employ empirical mode decomposition to transform the de-noised price into different intrinsic mode functions (IMFs). Finally, some complex long memory GARCH-M models are used to forecast different IMFs and a residual. Empirical results show that the proposed hybrid forecasting model WPD–EMD–ARMA–FIGARCH-M achieves significant effect during periods of extreme incidents. The robustness test shows that this hybrid model is superior to traditional models.

Keywords: Crude oil price forecasting; Structural break period; Wavelet de-noising; Empirical mode decomposition; Complex long memory GARCH-M models (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (21)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:543:y:2020:i:c:s0378437119319697

DOI: 10.1016/j.physa.2019.123532

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