Forecasting of stock return volatility plays an important role in the financial markets. 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 as a second source of information, which is used together with the GARCH model to predict the volatility of asset price returns. We call this NA-GARCH (news augmented GARCH) model. Our empirical investigation compares volatility prediction of returns of 12 different stocks (from two different stock markets), with 9 data sets for each stock. Our results clearly demonstrate that NA-GARCH provides a superior prediction of volatility than the “plain vanilla” GARCH model. 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.
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