Do search engine data improve financial time series volatility predictions in different market periods? An empirical analysis on major world financial indices.

Abstract

In this paper, we investigate the different influence of search engine data in different market periods on the improvement of the prediction of the financial time series volatility. We use the EGARCH and the EGARCH-SVI model. We analyze weekly data from the Dow Jones, FTSE 100 and Nikkei 225 market indices and the weekly search volume index (SVI) from google trends for market indices keywords. The main contribution of this paper is introducing limitations of the EGARCH-SVI model for forecasting the weekly volatility of the market index. Our results show that i) search engine data improve financial time series volatility predictions of the EGARCH-SVI model in market crisis periods with the bigger price volatility; and ii) search engine data is not improving the prediction of the financial time series volatility of the EGARCH-SVI model in a non-crisis periods with low price volatility in the market. This result also confirms the predictive power of the EGARCH-SVI model in crisis periods for different financial markets.