Crude oil prices and volatility prediction by a hybrid model based on kernel extreme learning machine

Hongli Niu; Yazhi Zhao; Hongli Niu; Yazhi Zhao

doi:10.3934/mbe.2021402

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 6: 8096-8122. doi: 10.3934/mbe.2021402

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Crude oil prices and volatility prediction by a hybrid model based on kernel extreme learning machine

Hongli Niu ^,,
Yazhi Zhao

School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China

Academic Editor: Kuen-Suan Chen

Received: 13 August 2021 Accepted: 15 September 2021 Published: 17 September 2021

In view of the important position of crude oil in the national economy and its contribution to various economic sectors, crude oil price and volatility prediction have become an increasingly hot issue that is concerned by practitioners and researchers. In this paper, a new hybrid forecasting model based on variational mode decomposition (VMD) and kernel extreme learning machine (KELM) is proposed to forecast the daily prices and 7-day volatility of Brent and WTI crude oil. The KELM has the advantage of less time consuming and lower parameter-sensitivity, thus showing fine prediction ability. The effectiveness of VMD-KELM model is verified by a comparative study with other hybrid models and their single models. Except various commonly used evaluation criteria, a recently-developed multi-scale composite complexity synchronization (MCCS) statistic is also utilized to evaluate the synchrony degree between the predictive and the actual values. The empirical results verify that 1) KELM model holds better performance than ELM and BP in crude oil and volatility forecasting; 2) VMD-based model outperforms the EEMD-based model; 3) The developed VMD-KELM model exhibits great superiority compared with other popular models not only for crude oil price, but also for volatility prediction.
- crude oil prediction,
- variational mode decomposition,
- kernel extreme learning machine,
- hybrid model,
- volatility
Citation: Hongli Niu, Yazhi Zhao. Crude oil prices and volatility prediction by a hybrid model based on kernel extreme learning machine[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 8096-8122. doi: 10.3934/mbe.2021402

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Abstract

In view of the important position of crude oil in the national economy and its contribution to various economic sectors, crude oil price and volatility prediction have become an increasingly hot issue that is concerned by practitioners and researchers. In this paper, a new hybrid forecasting model based on variational mode decomposition (VMD) and kernel extreme learning machine (KELM) is proposed to forecast the daily prices and 7-day volatility of Brent and WTI crude oil. The KELM has the advantage of less time consuming and lower parameter-sensitivity, thus showing fine prediction ability. The effectiveness of VMD-KELM model is verified by a comparative study with other hybrid models and their single models. Except various commonly used evaluation criteria, a recently-developed multi-scale composite complexity synchronization (MCCS) statistic is also utilized to evaluate the synchrony degree between the predictive and the actual values. The empirical results verify that 1) KELM model holds better performance than ELM and BP in crude oil and volatility forecasting; 2) VMD-based model outperforms the EEMD-based model; 3) The developed VMD-KELM model exhibits great superiority compared with other popular models not only for crude oil price, but also for volatility prediction.

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