The role of higher moments in predicting China's oil futures volatility: Evidence from machine learning models

IF 3.7 4区 经济学 Q1 BUSINESS, FINANCE Journal of Commodity Markets Pub Date : 2023-08-25 DOI:10.1016/j.jcomm.2023.100352
Hongwei Zhang , Xinyi Zhao , Wang Gao , Zibo Niu
{"title":"The role of higher moments in predicting China's oil futures volatility: Evidence from machine learning models","authors":"Hongwei Zhang ,&nbsp;Xinyi Zhao ,&nbsp;Wang Gao ,&nbsp;Zibo Niu","doi":"10.1016/j.jcomm.2023.100352","DOIUrl":null,"url":null,"abstract":"<div><p><span>This paper expands the emerging literature on volatility forecasting for China's oil market by exploring the predictive ability<span><span> of higher-order moments (skewness, kurtosis, hyperskewness, and hyperkurtosis) based on high-frequency data. Our investigation is originally based on the heterogeneous autoregressive (HAR) framework, but considering the possible multicollinearity and nonlinearity, it is extended to various machine learning (ML) models and combination </span>forecasting models. The results reveal that higher-order moments, including the two highest moments, always significantly improve predictive performance for the COVID-19 crisis. We further examine the interpretability of ML models and each factor's contribution to the prediction, finding that odd and even moments contain short- and long-term prediction information, respectively. This paper also highlights the effectiveness of ML models for capturing trends in oil futures volatility with higher-order moments and the satisfactory performance of combination forecasting models. Finally, we investigate the predictability of asymmetric </span></span>risk patterns and<span> obtain identical results. Our study has important implications for financial risk management, asset pricing, and portfolio allocation.</span></p></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"32 ","pages":"Article 100352"},"PeriodicalIF":3.7000,"publicationDate":"2023-08-25","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/S2405851323000429","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

Abstract

This paper expands the emerging literature on volatility forecasting for China's oil market by exploring the predictive ability of higher-order moments (skewness, kurtosis, hyperskewness, and hyperkurtosis) based on high-frequency data. Our investigation is originally based on the heterogeneous autoregressive (HAR) framework, but considering the possible multicollinearity and nonlinearity, it is extended to various machine learning (ML) models and combination forecasting models. The results reveal that higher-order moments, including the two highest moments, always significantly improve predictive performance for the COVID-19 crisis. We further examine the interpretability of ML models and each factor's contribution to the prediction, finding that odd and even moments contain short- and long-term prediction information, respectively. This paper also highlights the effectiveness of ML models for capturing trends in oil futures volatility with higher-order moments and the satisfactory performance of combination forecasting models. Finally, we investigate the predictability of asymmetric risk patterns and obtain identical results. Our study has important implications for financial risk management, asset pricing, and portfolio allocation.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
高矩在预测中国石油期货波动中的作用:来自机器学习模型的证据
本文通过探索基于高频数据的高阶矩(偏态、峰度、超峰度和超峰度)的预测能力,扩展了新兴的中国石油市场波动率预测文献。我们的研究最初是基于异构自回归(HAR)框架,但考虑到可能的多重共线性和非线性,将其扩展到各种机器学习(ML)模型和组合预测模型。结果表明,包括两个最高矩在内的高阶矩总是显著提高对COVID-19危机的预测性能。我们进一步研究了ML模型的可解释性和每个因素对预测的贡献,发现奇数和偶数时刻分别包含短期和长期预测信息。本文还强调了ML模型在捕捉具有高阶矩的石油期货波动趋势方面的有效性,以及组合预测模型的令人满意的性能。最后,我们研究了不对称风险模式的可预测性,得到了相同的结果。我们的研究对金融风险管理、资产定价和投资组合配置具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 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.
期刊最新文献
Carbon pricing and the commodity risk premium Have the causal effects between equities, oil prices, and monetary policy changed over time? Connectedness between green bonds, clean energy markets and carbon quota prices: Time and frequency dynamics Commodity market downturn: Systemic risk and spillovers during left tail events Forecasting crude oil returns with oil-related industry ESG indices
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1