货币政策对国际股市回报的预测力--来自 TV-ARMAX 模型的证据

Xiao Li, Wenjun Xue, Kaimeng Zhang
{"title":"货币政策对国际股市回报的预测力--来自 TV-ARMAX 模型的证据","authors":"Xiao Li, Wenjun Xue, Kaimeng Zhang","doi":"10.46557/001c.91484","DOIUrl":null,"url":null,"abstract":"In this paper, we apply the time-varying ARMA model with exogenous variable (TV-ARMAX) to examine the predictive power of monetary policy on international stock returns. This method allows time-varying coefficient estimates and uses time-dependent cumulated variation penalty to filter noisy outlier data points. Based on a wide range of 31 countries, our method robustly outperforms other popular methods including the simple linear-regression model (SLM), the vector autoregression and its variants (VAR, TV-VAR, and VARX) and the ARMA model with exogenous variable (ARMAX).","PeriodicalId":194045,"journal":{"name":"Asian Economics Letters","volume":"47 6","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Predictive Power of Monetary Policy on International Stock Market Returns—Evidence From TV-ARMAX Model\",\"authors\":\"Xiao Li, Wenjun Xue, Kaimeng Zhang\",\"doi\":\"10.46557/001c.91484\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we apply the time-varying ARMA model with exogenous variable (TV-ARMAX) to examine the predictive power of monetary policy on international stock returns. This method allows time-varying coefficient estimates and uses time-dependent cumulated variation penalty to filter noisy outlier data points. Based on a wide range of 31 countries, our method robustly outperforms other popular methods including the simple linear-regression model (SLM), the vector autoregression and its variants (VAR, TV-VAR, and VARX) and the ARMA model with exogenous variable (ARMAX).\",\"PeriodicalId\":194045,\"journal\":{\"name\":\"Asian Economics Letters\",\"volume\":\"47 6\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Asian Economics Letters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.46557/001c.91484\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asian Economics Letters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46557/001c.91484","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本文应用带外生变量的时变 ARMA 模型(TV-ARMAX)来研究货币政策对国际股票回报的预测能力。该方法允许时变系数估计,并使用随时间变化的累积变异惩罚来过滤噪声离群数据点。基于 31 个国家的广泛数据,我们的方法稳健地优于其他流行方法,包括简单线性回归模型(SLM)、向量自回归及其变体(VAR、TV-VAR 和 VARX)以及带外生变量的 ARMA 模型(ARMAX)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Predictive Power of Monetary Policy on International Stock Market Returns—Evidence From TV-ARMAX Model
In this paper, we apply the time-varying ARMA model with exogenous variable (TV-ARMAX) to examine the predictive power of monetary policy on international stock returns. This method allows time-varying coefficient estimates and uses time-dependent cumulated variation penalty to filter noisy outlier data points. Based on a wide range of 31 countries, our method robustly outperforms other popular methods including the simple linear-regression model (SLM), the vector autoregression and its variants (VAR, TV-VAR, and VARX) and the ARMA model with exogenous variable (ARMAX).
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
自引率
0.00%
发文量
0
×
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