中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Social media sentiment, model uncertainty, and volatility forecasting

文献类型:期刊论文

作者Lehrer, Steven3,4; Xie, Tian2; Zhang, Xinyu1
刊名ECONOMIC MODELLING
出版日期2021-09-01
卷号102页码:13
关键词Model averaging Volatility forecasting Social media Big data Sentiment analysis
ISSN号0264-9993
DOI10.1016/j.econmod.2021.105556
英文摘要Many economic indicators including consumer confidence indices used to forecast volatility or macroeconomic outcomes, are published with a considerable time lag. To obtain a timelier measure of consumer sentiment many central bank and economic researchers are turning towards using state-of-the-art text sentiment analysis tools. We examine if there are benefits for forecasting volatility from (i) incorporating a sentiment measure derived using deep learning from Twitter messages at the 1-min level, and (ii) acknowledging specification uncertainty of the lag index in the heterogeneous autoregression (HAR) model. We present evidence from an out of sample forecasting exercise that suggests including social media sentiment can significantly improve the forecasting accuracy of a popular volatility index, particularly in short time horizons. Further, our results document large gains in predictive accuracy from a newly proposed estimator that allows for model uncertainty in the specification of the lag index when using a HAR estimator.
资助项目Natural Science Foundation of China[71925007] ; Natural Science Foundation of China[72091212] ; Natural Science Foundation of China[11688101] ; Natural Science Foundation of China[71701175] ; Fundamental Research Funds for the Central Universities ; National Key R&D Program of China[2020AAA0105200] ; Beijing Academy of Artificial Intelligence ; Youth Innovation Promotion Association of Chinese Academy of Sciences ; SSHRC
WOS研究方向Business & Economics
语种英语
WOS记录号WOS:000680417500005
出版者ELSEVIER
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/59011]  
专题中国科学院数学与系统科学研究院
通讯作者Xie, Tian
作者单位1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
2.Shanghai Univ Finance & Econ, Coll Business, Shanghai, Peoples R China
3.NBER, Cambridge, MA 02138 USA
4.Queens Univ, Kingston, ON, Canada
推荐引用方式
GB/T 7714
Lehrer, Steven,Xie, Tian,Zhang, Xinyu. Social media sentiment, model uncertainty, and volatility forecasting[J]. ECONOMIC MODELLING,2021,102:13.
APA Lehrer, Steven,Xie, Tian,&Zhang, Xinyu.(2021).Social media sentiment, model uncertainty, and volatility forecasting.ECONOMIC MODELLING,102,13.
MLA Lehrer, Steven,et al."Social media sentiment, model uncertainty, and volatility forecasting".ECONOMIC MODELLING 102(2021):13.

入库方式: OAI收割

来源:数学与系统科学研究院

浏览0
下载0
收藏0
其他版本

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。