Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258789 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 15 [Issue:] 2 [Article No.:] 66 [Publisher:] MDPI [Place:] Basel [Year:] 2022 [Pages:] 1-25
Publisher: 
MDPI, Basel
Abstract: 
This paper investigates the role of investor attention in forecasting realized volatility for fourteen international stock markets, by means of Google Trends data, over the sample period January 2004 through November 2021. We devise an augmented Empirical Similarity model that combines three volatility components, defined over different time horizons, using the similarity measure between lagged Google search queries and volatility. Results show that investor attention positively affects future volatility in the short-run. The effect of investor attention is likely to reverse in the long-run, consistently with the price pressure hypothesis. The proposed model demonstrates important gains in terms of volatility forecast accuracy and outperforms highly competitive models.
Subjects: 
realized volatility
heterogeneous autoregressive model
investor attention
empirical similarity
JEL: 
C22
C52
G17
G41
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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