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Can internet search queries help to predict stock market volatility?

Listed author(s):
  • Dimpfl, Thomas
  • Jank, Stephan

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.

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File URL: https://www.econstor.eu/bitstream/10419/52242/1/671988344.pdf
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Paper provided by University of Cologne, Centre for Financial Research (CFR) in its series CFR Working Papers with number 11-15.

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Date of creation: 2011
Handle: RePEc:zbw:cfrwps:1115
Contact details of provider: Postal:
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Phone: 0221 / 470 5607
Fax: 0221 / 470 5179
Web page: http://cfr-cologne.de/english/version06/html/home.php
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