Cargando…

Volatility forecasting of crude oil futures based on a genetic algorithm regularization online extreme learning machine with a forgetting factor: The role of news during the COVID-19 pandemic

The outbreak of news and opinions during the COVID-19 pandemic is unprecedented in this age of rapid dissemination of information. The ensuing uncertainty has led to the emergence of heightened volatility in prices of crude oil futures. Whether such news has predictive value for the volatility of cr...

Descripción completa

Detalles Bibliográficos
Autores principales: Weng, Futian, Zhang, Hongwei, Yang, Cai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434824/
https://www.ncbi.nlm.nih.gov/pubmed/34539033
http://dx.doi.org/10.1016/j.resourpol.2021.102148
_version_ 1783751685045223424
author Weng, Futian
Zhang, Hongwei
Yang, Cai
author_facet Weng, Futian
Zhang, Hongwei
Yang, Cai
author_sort Weng, Futian
collection PubMed
description The outbreak of news and opinions during the COVID-19 pandemic is unprecedented in this age of rapid dissemination of information. The ensuing uncertainty has led to the emergence of heightened volatility in prices of crude oil futures. Whether such news has predictive value for the volatility of crude oil futures during the COVID-19 pandemic is examined in this research. We proposed a modeling framework, genetic algorithm regularization online extreme learning machine with forgetting factor (GA-RFOS-ELM), to estimate the effects of news during the COVID-19 pandemic on the volatility of crude oil futures. GA-RFOS-ELM could learn block-by-block with fixed or varying block size when considering the block own valid period. The experimental results illustrate that news during the COVID-19 pandemic has more predictive information, which is crucial for short-term volatility forecasting of crude oil futures. The novel approach illustrates that online update learning ability is needed during the COVID-19 pandemic, which could be effective and efficient in volatility forecasting of crude oil futures. The contributions of our study are significant for investors and administrators to predict and understand the behavior of volatility during the COVID-19 pandemic.
format Online
Article
Text
id pubmed-8434824
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Elsevier Ltd.
record_format MEDLINE/PubMed
spelling pubmed-84348242021-09-13 Volatility forecasting of crude oil futures based on a genetic algorithm regularization online extreme learning machine with a forgetting factor: The role of news during the COVID-19 pandemic Weng, Futian Zhang, Hongwei Yang, Cai Resour Policy Article The outbreak of news and opinions during the COVID-19 pandemic is unprecedented in this age of rapid dissemination of information. The ensuing uncertainty has led to the emergence of heightened volatility in prices of crude oil futures. Whether such news has predictive value for the volatility of crude oil futures during the COVID-19 pandemic is examined in this research. We proposed a modeling framework, genetic algorithm regularization online extreme learning machine with forgetting factor (GA-RFOS-ELM), to estimate the effects of news during the COVID-19 pandemic on the volatility of crude oil futures. GA-RFOS-ELM could learn block-by-block with fixed or varying block size when considering the block own valid period. The experimental results illustrate that news during the COVID-19 pandemic has more predictive information, which is crucial for short-term volatility forecasting of crude oil futures. The novel approach illustrates that online update learning ability is needed during the COVID-19 pandemic, which could be effective and efficient in volatility forecasting of crude oil futures. The contributions of our study are significant for investors and administrators to predict and understand the behavior of volatility during the COVID-19 pandemic. Elsevier Ltd. 2021-10 2021-05-21 /pmc/articles/PMC8434824/ /pubmed/34539033 http://dx.doi.org/10.1016/j.resourpol.2021.102148 Text en © 2021 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Weng, Futian
Zhang, Hongwei
Yang, Cai
Volatility forecasting of crude oil futures based on a genetic algorithm regularization online extreme learning machine with a forgetting factor: The role of news during the COVID-19 pandemic
title Volatility forecasting of crude oil futures based on a genetic algorithm regularization online extreme learning machine with a forgetting factor: The role of news during the COVID-19 pandemic
title_full Volatility forecasting of crude oil futures based on a genetic algorithm regularization online extreme learning machine with a forgetting factor: The role of news during the COVID-19 pandemic
title_fullStr Volatility forecasting of crude oil futures based on a genetic algorithm regularization online extreme learning machine with a forgetting factor: The role of news during the COVID-19 pandemic
title_full_unstemmed Volatility forecasting of crude oil futures based on a genetic algorithm regularization online extreme learning machine with a forgetting factor: The role of news during the COVID-19 pandemic
title_short Volatility forecasting of crude oil futures based on a genetic algorithm regularization online extreme learning machine with a forgetting factor: The role of news during the COVID-19 pandemic
title_sort volatility forecasting of crude oil futures based on a genetic algorithm regularization online extreme learning machine with a forgetting factor: the role of news during the covid-19 pandemic
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434824/
https://www.ncbi.nlm.nih.gov/pubmed/34539033
http://dx.doi.org/10.1016/j.resourpol.2021.102148
work_keys_str_mv AT wengfutian volatilityforecastingofcrudeoilfuturesbasedonageneticalgorithmregularizationonlineextremelearningmachinewithaforgettingfactortheroleofnewsduringthecovid19pandemic
AT zhanghongwei volatilityforecastingofcrudeoilfuturesbasedonageneticalgorithmregularizationonlineextremelearningmachinewithaforgettingfactortheroleofnewsduringthecovid19pandemic
AT yangcai volatilityforecastingofcrudeoilfuturesbasedonageneticalgorithmregularizationonlineextremelearningmachinewithaforgettingfactortheroleofnewsduringthecovid19pandemic