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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2021
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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 |
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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 |
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