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

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 b...

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Autores principales:	Hongli Niu, Yazhi Zhao
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	crude oil prediction variational mode decomposition kernel extreme learning machine hybrid model volatility Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/c1fc9f414c304d1cb44411c63007604e
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spelling	oai:doaj.org-article:c1fc9f414c304d1cb44411c63007604e2021-11-24T00:54:21ZCrude oil prices and volatility prediction by a hybrid model based on kernel extreme learning machine10.3934/mbe.20214021551-0018https://doaj.org/article/c1fc9f414c304d1cb44411c63007604e2021-09-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021402?viewType=HTMLhttps://doaj.org/toc/1551-0018In 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.Hongli NiuYazhi ZhaoAIMS Pressarticlecrude oil predictionvariational mode decompositionkernel extreme learning machinehybrid modelvolatilityBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 8096-8122 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	crude oil prediction variational mode decomposition kernel extreme learning machine hybrid model volatility Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	crude oil prediction variational mode decomposition kernel extreme learning machine hybrid model volatility Biotechnology TP248.13-248.65 Mathematics QA1-939 Hongli Niu Yazhi Zhao Crude oil prices and volatility prediction by a hybrid model based on kernel extreme learning machine
description	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.
format	article
author	Hongli Niu Yazhi Zhao
author_facet	Hongli Niu Yazhi Zhao
author_sort	Hongli Niu
title	Crude oil prices and volatility prediction by a hybrid model based on kernel extreme learning machine
title_short	Crude oil prices and volatility prediction by a hybrid model based on kernel extreme learning machine
title_full	Crude oil prices and volatility prediction by a hybrid model based on kernel extreme learning machine
title_fullStr	Crude oil prices and volatility prediction by a hybrid model based on kernel extreme learning machine
title_full_unstemmed	Crude oil prices and volatility prediction by a hybrid model based on kernel extreme learning machine
title_sort	crude oil prices and volatility prediction by a hybrid model based on kernel extreme learning machine
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/c1fc9f414c304d1cb44411c63007604e
work_keys_str_mv	AT hongliniu crudeoilpricesandvolatilitypredictionbyahybridmodelbasedonkernelextremelearningmachine AT yazhizhao crudeoilpricesandvolatilitypredictionbyahybridmodelbasedonkernelextremelearningmachine
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Crude oil prices and volatility prediction by a hybrid model based on kernel extreme learning machine

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