Forecasting the volatility of financial assets’ returns using high-frequency data and quantifiers of the information environment
Today, news information is constantly generated and quickly disseminated in the digital information environment. Emotions in news information influence people's actions and behavior in the real world. Thus, a systematic analysis of news information will make it possible to predict both short- and medium-term emotional transformations of consumers of news information, as well as the impact of this on the real world. In this study, it is proposed to use a systematic analysis of news information to predict the volatility of returns of financial assets. We developed and evaluated a model for predicting the volatility of financial assets returns using high-frequency data and quantifiers of the information environment. Evaluation of our volatility model (sentiment based realized volatility model - SRV model) in option trading revealed that a combination of high frequency volatility estimators with an effective system of quantifiers of emotions in news information about financial assets is useful in forecasting the volatility of stock returns. For Bank of America stock, it was found that the SRV model formulated based on volatility estimates by the realized kernel estimator and negative and positive emotions in news information is effective in predicting the volatility of Bank of America stock. The study also found that the SRV model based on the modulated realized covariance estimator and positive and negative emotions in news information gives accurate forecast of volatility for Coca-Cola and Microsoft stocks. Based on the results of the empirical analysis, it was concluded that a systematic analysis of news information is useful for predicting the volatility of financial assets returns, however its effectiveness is company specific.