Forecasting volatility with empirical similarity and Google Trends

Alain Hamid and Moritz Heiden

Journal of Economic Behavior & Organization, 2015, vol. 117, issue C, 62-81

Abstract: This paper proposes an empirical similarity approach to forecast weekly volatility by using search engine data as a measure of investors attention to the stock market index. Our model is assumption free with respect to the underlying process of investors attention and significantly outperforms conventional time-series models in an out-of-sample forecasting framework. We find that especially in high-volatility market phases prediction accuracy increases together with investor attention. The practical implications for risk management are highlighted in a Value-at-Risk forecasting exercise, where our model produces significantly more accurate forecasts while requiring less capital due to fewer overpredictions.

Keywords: Empirical similarity; Google trends; Investor attention; Volatility; Forecasting (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (63)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jeborg:v:117:y:2015:i:c:p:62-81

DOI: 10.1016/j.jebo.2015.06.005

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