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Can Internet Search Queries Help to Predict Stock Market Volatility?
Author(s) -
Dimpfl Thomas,
Jank Stephan
Publication year - 2016
Publication title -
european financial management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.311
H-Index - 64
eISSN - 1468-036X
pISSN - 1354-7798
DOI - 10.1111/eufm.12058
Subject(s) - volatility (finance) , volatility smile , implied volatility , volatility swap , volatility risk premium , stock market , econometrics , economics , forward volatility , financial economics , the internet , stochastic volatility , stock (firearms) , autoregressive model , stock market index , computer science , engineering , geography , world wide web , mechanical engineering , context (archaeology) , archaeology
We study the dynamics of stock market volatility and retail investors' attention to the stock market. The latter is measured by internet search queries related to the leading stock market index. We find a strong co‐movement of the Dow Jones' realised volatility and the volume of search queries for its name. Furthermore, search queries Granger‐cause volatility: a heightened number of searches today is followed by an increase in volatility tomorrow. Including search queries in autoregressive models of realised volatility improves volatility forecasts in‐sample, out‐of‐sample, for different forecasting horizons, and in particular in high‐volatility phases.