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http://hdl.handle.net/10419/52242
  
Title:Can internet search queries help to predict stock market volatility? PDF Logo
Authors:Dimpfl, Thomas
Jank, Stephan
Issue Date:2011
Series/Report no.:CFR working paper 11-15
Abstract:This paper studies the dynamics of stock market volatility and retail investor attention measured by internet search queries. We find a strong co-movement of stock market indices' realized volatility and the search queries for their names. Furthermore, Granger causality is bi-directional: high searches follow high volatility, and high volatility follows high searches. Using the latter feedback effect to predict volatility we find that search queries contain additional information about market volatility. They help to improve volatility forecasts in-sample and out-of-sample as well as for different forecasting horizons. Search queries are particularly useful to predict volatility in high-volatility phases.
Subjects:realized volatility
forecasting
investor behavior
noise trader
search engine data
JEL:G10
G14
G17
Document Type:Working Paper
Appears in Collections:CFR Working Papers, Centre for Financial Research (CFR), Universität Köln

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