Forecasting realised volatility using regime-switching models

IF 4.8 2区 经济学 Q1 BUSINESS, FINANCE
Yi Ding , Dimos Kambouroudis , David G. McMillan
{"title":"Forecasting realised volatility using regime-switching models","authors":"Yi Ding ,&nbsp;Dimos Kambouroudis ,&nbsp;David G. McMillan","doi":"10.1016/j.iref.2025.104171","DOIUrl":null,"url":null,"abstract":"<div><div>This paper extends standard AR and HAR models for realised volatility (RV) forecasting to include nonlinearity through two broad regime-switching approaches, the smooth transition and Markov-switching methods. Using daily data for eight international stock markets over the period 2007–2021, a comprehensive comparison is provided using a range of forecast tests that includes statistical and economic (risk management) based metrics. The results show that regime-switching models provide a better in-sample fit and out-of-sample forecasting, although this latter result is less clear-cut at the daily horizon. In comparing the two nonlinear approaches, we find that the abrupt transition technique of the Markov-switching model is preferred to the smooth transition one. It is believed that our results will be of interest to those especially engaged in risk management practice as well as for those modelling market behaviour.</div></div>","PeriodicalId":14444,"journal":{"name":"International Review of Economics & Finance","volume":"101 ","pages":"Article 104171"},"PeriodicalIF":4.8000,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Review of Economics & Finance","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S105905602500334X","RegionNum":2,"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 extends standard AR and HAR models for realised volatility (RV) forecasting to include nonlinearity through two broad regime-switching approaches, the smooth transition and Markov-switching methods. Using daily data for eight international stock markets over the period 2007–2021, a comprehensive comparison is provided using a range of forecast tests that includes statistical and economic (risk management) based metrics. The results show that regime-switching models provide a better in-sample fit and out-of-sample forecasting, although this latter result is less clear-cut at the daily horizon. In comparing the two nonlinear approaches, we find that the abrupt transition technique of the Markov-switching model is preferred to the smooth transition one. It is believed that our results will be of interest to those especially engaged in risk management practice as well as for those modelling market behaviour.
使用状态切换模型预测已实现的波动性
本文扩展了标准的AR和HAR模型,通过两种广泛的状态切换方法,平滑过渡和马尔可夫切换方法,用于实现波动率(RV)预测,以包括非线性。利用2007-2021年期间八个国际股票市场的每日数据,使用一系列预测测试,包括基于统计和经济(风险管理)的指标,提供了全面的比较。结果表明,状态切换模型提供了更好的样本内拟合和样本外预测,尽管后者的结果在日常水平上不太明确。通过对两种非线性方法的比较,我们发现马尔可夫切换模型的突变技术优于平滑过渡技术。我们相信,我们的结果将对那些从事风险管理实践的人以及那些模拟市场行为的人感兴趣。
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 求助全文
来源期刊
CiteScore
7.30
自引率
2.20%
发文量
253
期刊介绍: The International Review of Economics & Finance (IREF) is a scholarly journal devoted to the publication of high quality theoretical and empirical articles in all areas of international economics, macroeconomics and financial economics. Contributions that facilitate the communications between the real and the financial sectors of the economy are of particular interest.
×
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