Crude oil price forecasting based on a novel hybrid long memory GARCH-M and wavelet analysis model
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.
- Publication:
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Physica A Statistical Mechanics and its Applications
- Pub Date:
- April 2020
- DOI:
- 10.1016/j.physa.2019.123532
- Bibcode:
- 2020PhyA..54323532L
- Keywords:
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- Crude oil price forecasting;
- Structural break period;
- Wavelet de-noising;
- Empirical mode decomposition;
- Complex long memory GARCH-M models