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Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models

This study evaluates whether CBOE crude oil volatility index (OVX) owns forecasting ability for China’s oil futures volatility using Markov-regime mixed data sampling (MS-MIDAS) models. In-sample empirical result shows that, OVX can significantly lead to high future short-term, middle-term and long-...

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Detalles Bibliográficos
Autores principales: Lu, Xinjie, Ma, Feng, Wang, Jiqian, Wang, Jianqiong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7462548/
https://www.ncbi.nlm.nih.gov/pubmed/32904908
http://dx.doi.org/10.1016/j.energy.2020.118743
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author Lu, Xinjie
Ma, Feng
Wang, Jiqian
Wang, Jianqiong
author_facet Lu, Xinjie
Ma, Feng
Wang, Jiqian
Wang, Jianqiong
author_sort Lu, Xinjie
collection PubMed
description This study evaluates whether CBOE crude oil volatility index (OVX) owns forecasting ability for China’s oil futures volatility using Markov-regime mixed data sampling (MS-MIDAS) models. In-sample empirical result shows that, OVX can significantly lead to high future short-term, middle-term and long-term volatilities with regard to Chinese oil futures market. Moreover, our proposed model, the Markov-regime MIDAS with including the OVX (MS-MIDAS-RV-OVX), significantly outperforms the MIDAS and other competing models. Unsurprising results further confirm that OVX indeed contain predictive information for oil realized volatility (especially significant and robust in middle-term and long-term horizons) and regime switching is useful to deal with the structural break within the energy market. We carry out economic value analysis and discuss OVX’s asymmetric effects concerning different trading hours and good (bad) OVX, and find OVX performs better in day-time trading hours and the good OVX is more predictive for the oil futures RV than the bad OVX. The further discussion also confirms our previous conclusions are robust during the highly volatile period of the COVID-19 pandemic.
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spelling pubmed-74625482020-09-02 Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models Lu, Xinjie Ma, Feng Wang, Jiqian Wang, Jianqiong Energy (Oxf) Article This study evaluates whether CBOE crude oil volatility index (OVX) owns forecasting ability for China’s oil futures volatility using Markov-regime mixed data sampling (MS-MIDAS) models. In-sample empirical result shows that, OVX can significantly lead to high future short-term, middle-term and long-term volatilities with regard to Chinese oil futures market. Moreover, our proposed model, the Markov-regime MIDAS with including the OVX (MS-MIDAS-RV-OVX), significantly outperforms the MIDAS and other competing models. Unsurprising results further confirm that OVX indeed contain predictive information for oil realized volatility (especially significant and robust in middle-term and long-term horizons) and regime switching is useful to deal with the structural break within the energy market. We carry out economic value analysis and discuss OVX’s asymmetric effects concerning different trading hours and good (bad) OVX, and find OVX performs better in day-time trading hours and the good OVX is more predictive for the oil futures RV than the bad OVX. The further discussion also confirms our previous conclusions are robust during the highly volatile period of the COVID-19 pandemic. Elsevier Ltd. 2020-12-01 2020-09-01 /pmc/articles/PMC7462548/ /pubmed/32904908 http://dx.doi.org/10.1016/j.energy.2020.118743 Text en © 2020 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
Lu, Xinjie
Ma, Feng
Wang, Jiqian
Wang, Jianqiong
Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models
title Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models
title_full Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models
title_fullStr Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models
title_full_unstemmed Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models
title_short Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models
title_sort examining the predictive information of cboe ovx on china’s oil futures volatility: evidence from ms-midas models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7462548/
https://www.ncbi.nlm.nih.gov/pubmed/32904908
http://dx.doi.org/10.1016/j.energy.2020.118743
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