功能性波动预测

IF 3.4 3区 经济学 Q1 ECONOMICS
Yingwen Tan, Zhensi Tan, Yinfen Tang, Zhiyuan Zhang
{"title":"功能性波动预测","authors":"Yingwen Tan,&nbsp;Zhensi Tan,&nbsp;Yinfen Tang,&nbsp;Zhiyuan Zhang","doi":"10.1002/for.3170","DOIUrl":null,"url":null,"abstract":"<p>Widely used volatility forecasting methods are usually based on low-frequency time series models. Although some of them employ high-frequency observations, these intraday data are often summarized into low-frequency <i>point</i> statistics, for example, daily realized measures, before being incorporated into a forecasting model. This paper contributes to the volatility forecasting literature by instead predicting the next-period intraday volatility curve via a <i>functional</i> time series forecasting approach. Asymptotic theory related to the estimation of latent volatility curves via functional principal analysis is formally established, laying a solid theoretical foundation of the proposed forecasting method. In contrast with nonfunctional methods, the proposed functional approach fully exploits the rich intraday information and hence leads to more accurate volatility forecasts. This is confirmed by extensive comparisons between the proposed method and those widely used nonfunctional methods in both Monte Carlo simulations and an empirical study on a number of stocks and equity indices from the Chinese market.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 8","pages":"3009-3034"},"PeriodicalIF":3.4000,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Functional volatility forecasting\",\"authors\":\"Yingwen Tan,&nbsp;Zhensi Tan,&nbsp;Yinfen Tang,&nbsp;Zhiyuan Zhang\",\"doi\":\"10.1002/for.3170\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Widely used volatility forecasting methods are usually based on low-frequency time series models. Although some of them employ high-frequency observations, these intraday data are often summarized into low-frequency <i>point</i> statistics, for example, daily realized measures, before being incorporated into a forecasting model. This paper contributes to the volatility forecasting literature by instead predicting the next-period intraday volatility curve via a <i>functional</i> time series forecasting approach. Asymptotic theory related to the estimation of latent volatility curves via functional principal analysis is formally established, laying a solid theoretical foundation of the proposed forecasting method. In contrast with nonfunctional methods, the proposed functional approach fully exploits the rich intraday information and hence leads to more accurate volatility forecasts. This is confirmed by extensive comparisons between the proposed method and those widely used nonfunctional methods in both Monte Carlo simulations and an empirical study on a number of stocks and equity indices from the Chinese market.</p>\",\"PeriodicalId\":47835,\"journal\":{\"name\":\"Journal of Forecasting\",\"volume\":\"43 8\",\"pages\":\"3009-3034\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-06-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Forecasting\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/for.3170\",\"RegionNum\":3,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Forecasting","FirstCategoryId":"96","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/for.3170","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0

摘要

广泛使用的波动率预测方法通常基于低频时间序列模型。尽管其中一些方法采用了高频观测数据,但在将这些日内数据纳入预测模型之前,通常会将其归纳为低频点统计数据,例如日已实现测量值。本文通过函数式时间序列预测方法来预测下一期的盘中波动率曲线,从而为波动率预测文献做出了贡献。本文正式建立了与通过函数主分析估计潜在波动率曲线相关的渐近理论,为所提出的预测方法奠定了坚实的理论基础。与非函数式方法相比,所提出的函数式方法充分利用了丰富的盘中信息,因此能得出更准确的波动率预测。通过蒙特卡洛模拟和对中国市场上一些股票和股票指数的实证研究,对所提出的方法和广泛使用的非函数方法进行了广泛的比较,证实了这一点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Functional volatility forecasting

Widely used volatility forecasting methods are usually based on low-frequency time series models. Although some of them employ high-frequency observations, these intraday data are often summarized into low-frequency point statistics, for example, daily realized measures, before being incorporated into a forecasting model. This paper contributes to the volatility forecasting literature by instead predicting the next-period intraday volatility curve via a functional time series forecasting approach. Asymptotic theory related to the estimation of latent volatility curves via functional principal analysis is formally established, laying a solid theoretical foundation of the proposed forecasting method. In contrast with nonfunctional methods, the proposed functional approach fully exploits the rich intraday information and hence leads to more accurate volatility forecasts. This is confirmed by extensive comparisons between the proposed method and those widely used nonfunctional methods in both Monte Carlo simulations and an empirical study on a number of stocks and equity indices from the Chinese market.

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
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
×
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