News augmented GARCH(1,1) model for volatility prediction

Abstract

Forecasting the volatility of stock return plays an important role in the financial markets. The Generalized Autoregressive Conditional Heterscedasticity (GARCH) model is one of the most common models used for predicting asset price volatility from the return time series. In this study, we have considered quantified news sentiment and its impact on the movement of asset prices as a second source of information, which is used together with the asset time series data to predict the volatility of asset price returns.We call this news augmented GARCH (NA-GARCH) model. Our empirical investigation compares volatility prediction of returns of 12 different stocks (from two different stock markets), with nine datasets for each stock. Our results demonstrate that NA-GARCH provides a superior prediction of volatility than the 'plain vanilla' GARCH and exponential GARCH models. These results vindicate some recent findings regarding the utility of news sentiment as a predictor of volatility and also vindicate the utility of our novel model structure combining the proxies for past news sentiments and the past asset price returns.