Volatility Forecast with GARCH Model and News Analytics

In this study we investigate how the prediction of future volatility is improved by using news (meta)data. We use three input time series, namely: (i) market data, (ii) news sentiment impact scores, as explained by Yu (2014), and (iii) the news volume. We compare the results of predicting volatility by using a “vanilla” GARCH model, which uses market data only, and the news enhanced GARCH, as described above. Finally, the forecasted volatility is compared with the realized volatility, allowing an assessment of the robustness and precision of the model. RavenPack and Thomson Reuters provided news data and market data, respectively. The main findings are that the inclusion of scheduled news and the inclusion of news volume characterized by negative sentiment improve the forecasted volatility. The added value of scheduled news to volatility predictions is in line with Li and Engle (1998)