Stock volatility modelling with augmented GARCH model with jumps

Knowing the characteristics of news in numerical indices one can use them in mathematical and statistical models and automated trading systems. Currently, the tools of the news analytics have been increasingly used by traders in the U.S. and Europe. The interest in news analytics is related to the ability to predict changes of prices, volatility and trading volume on the stock market. The emphasis of the paper is on assessing the added value of using news analytics data in improving the explanatory power of the GARCH-Jump model. Based on empirical evidences for some of FTSE100 companies, the paper examines two GARCH models with jumps. First we consider the well-known GARCH model with autoregressive conditional jump intensity proposed in [1]. Then we introduce the GARCH-Jumps model augmented with news intensity and obtain some empirical results. The main assumption of the model is that jump intensity might change over time and that jump intensity depends linearly on the number of news (the news intensity). The comparison of the values of log likelihood supports the hypothesis of impact of news on the jump intensity of volatility.