七国集团股市波动对预测油价波动的非对称效应:量化自回归模型的证据

IF 3.7 4区 经济学 Q1 BUSINESS, FINANCE
Feipeng Zhang , Hongfu Gao , Di Yuan
{"title":"七国集团股市波动对预测油价波动的非对称效应:量化自回归模型的证据","authors":"Feipeng Zhang ,&nbsp;Hongfu Gao ,&nbsp;Di Yuan","doi":"10.1016/j.jcomm.2024.100409","DOIUrl":null,"url":null,"abstract":"<div><p>This paper investigates the asymmetric effect of G7 stock market volatility on predicting oil price volatility under different oil market conditions by using the quantile autoregression model. Both in- and out-of-sample results demonstrate the prediction superiority and effectiveness of the quantile autoregression model. The US and Canada's stock markets exhibit the strongest predictive ability across the entire distribution, while the UK demonstrates strong predictive power specifically during periods of high oil price volatility. Japan, Germany, France, and Italy as oil importers can predict low and median oil volatility. The strong predictability of G7 stock volatility may be attributable to their significant impact on the business cycle and investor sentiment. This asymmetric prediction ability arises not only from the average volatility shocks at various quantiles but also from the bad and good stock volatility at different quantiles. Further research suggests that bad stock volatility appears to be more predictable than good stock volatility, especially in high oil price fluctuations. Furthermore, the superiority and effectiveness of the quantile autoregression model in predicting oil volatility are proven to be applicable to emerging markets. This study may provide useful insights for policymakers, businesses, and investors to improve crude oil risk prediction and risk management under different market conditions.</p></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"35 ","pages":"Article 100409"},"PeriodicalIF":3.7000,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The asymmetric effect of G7 stock market volatility on predicting oil price volatility: Evidence from quantile autoregression model\",\"authors\":\"Feipeng Zhang ,&nbsp;Hongfu Gao ,&nbsp;Di Yuan\",\"doi\":\"10.1016/j.jcomm.2024.100409\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This paper investigates the asymmetric effect of G7 stock market volatility on predicting oil price volatility under different oil market conditions by using the quantile autoregression model. Both in- and out-of-sample results demonstrate the prediction superiority and effectiveness of the quantile autoregression model. The US and Canada's stock markets exhibit the strongest predictive ability across the entire distribution, while the UK demonstrates strong predictive power specifically during periods of high oil price volatility. Japan, Germany, France, and Italy as oil importers can predict low and median oil volatility. The strong predictability of G7 stock volatility may be attributable to their significant impact on the business cycle and investor sentiment. This asymmetric prediction ability arises not only from the average volatility shocks at various quantiles but also from the bad and good stock volatility at different quantiles. Further research suggests that bad stock volatility appears to be more predictable than good stock volatility, especially in high oil price fluctuations. Furthermore, the superiority and effectiveness of the quantile autoregression model in predicting oil volatility are proven to be applicable to emerging markets. This study may provide useful insights for policymakers, businesses, and investors to improve crude oil risk prediction and risk management under different market conditions.</p></div>\",\"PeriodicalId\":45111,\"journal\":{\"name\":\"Journal of Commodity Markets\",\"volume\":\"35 \",\"pages\":\"Article 100409\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-05-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Commodity Markets\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S240585132400028X\",\"RegionNum\":4,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Commodity Markets","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S240585132400028X","RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 0

摘要

本文利用量子自回归模型研究了在不同石油市场条件下,七国集团股票市场波动对预测石油价格波动的非对称效应。样本内和样本外的结果都证明了量子自回归模型的预测优势和有效性。美国和加拿大的股票市场在整个分布中表现出最强的预测能力,而英国则在油价高波动期表现出很强的预测能力。作为石油进口国的日本、德国、法国和意大利可以预测石油波动的低值和中值。七国集团股票波动的强预测性可能是由于它们对商业周期和投资者情绪的重大影响。这种非对称预测能力不仅来自于不同数量级的平均波动率冲击,也来自于不同数量级的坏股票波动率和好股票波动率。进一步的研究表明,坏股票波动似乎比好股票波动更容易预测,尤其是在高油价波动时。此外,量化自回归模型在预测石油波动方面的优越性和有效性也被证明适用于新兴市场。本研究可为政策制定者、企业和投资者在不同市场条件下改进原油风险预测和风险管理提供有益的启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The asymmetric effect of G7 stock market volatility on predicting oil price volatility: Evidence from quantile autoregression model

This paper investigates the asymmetric effect of G7 stock market volatility on predicting oil price volatility under different oil market conditions by using the quantile autoregression model. Both in- and out-of-sample results demonstrate the prediction superiority and effectiveness of the quantile autoregression model. The US and Canada's stock markets exhibit the strongest predictive ability across the entire distribution, while the UK demonstrates strong predictive power specifically during periods of high oil price volatility. Japan, Germany, France, and Italy as oil importers can predict low and median oil volatility. The strong predictability of G7 stock volatility may be attributable to their significant impact on the business cycle and investor sentiment. This asymmetric prediction ability arises not only from the average volatility shocks at various quantiles but also from the bad and good stock volatility at different quantiles. Further research suggests that bad stock volatility appears to be more predictable than good stock volatility, especially in high oil price fluctuations. Furthermore, the superiority and effectiveness of the quantile autoregression model in predicting oil volatility are proven to be applicable to emerging markets. This study may provide useful insights for policymakers, businesses, and investors to improve crude oil risk prediction and risk management under different market conditions.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
5.70
自引率
2.40%
发文量
53
期刊介绍: The purpose of the journal is also to stimulate international dialog among academics, industry participants, traders, investors, and policymakers with mutual interests in commodity markets. The mandate for the journal is to present ongoing work within commodity economics and finance. Topics can be related to financialization of commodity markets; pricing, hedging, and risk analysis of commodity derivatives; risk premia in commodity markets; real option analysis for commodity project investment and production; portfolio allocation including commodities; forecasting in commodity markets; corporate finance for commodity-exposed corporations; econometric/statistical analysis of commodity markets; organization of commodity markets; regulation of commodity markets; local and global commodity trading; and commodity supply chains. Commodity markets in this context are energy markets (including renewables), metal markets, mineral markets, agricultural markets, livestock and fish markets, markets for weather derivatives, emission markets, shipping markets, water, and related markets. This interdisciplinary and trans-disciplinary journal will cover all commodity markets and is thus relevant for a broad audience. Commodity markets are not only of academic interest but also highly relevant for many practitioners, including asset managers, industrial managers, investment bankers, risk managers, and also policymakers in governments, central banks, and supranational institutions.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信