Forecasting Volatility With Empirical Similarity And Google Trends
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Forecasting Volatility with Empirical Similarity and Google Trends
Author | : Moritz Heiden |
Publisher | : |
Total Pages | : |
Release | : 2015 |
Genre | : |
ISBN | : |
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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.
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