Abstract:
Volatility modelling and forecasting in the financial market is significant in risk management, monetary policy making, security valuation
and portfolio creation. Standard volatility models use historical asset
price returns to model and predict volatility. The purpose of this study
is to add an exogenous variable to the standard volatility model. The
exogenous variables used in this research are the news sentiments from
Safaricom news articles extracted from Business daily, a Kenyan news
publisher that consistently publishes business news. These news sentiments are the counts of positive and negative articles. Safaricom was
chosen due to its huge market capitalization compared to other stocks in
Kenya and it also has enough news data points for analysis. The Safaricom news articles were classified into either positive or negative using
Support Vector Machine. The volatility model that incorporates news
sentiments was formulated and its modelling and forecasting capabilities was compared to some standard volatility models. The empirical
results indicate that the news sentiments augmented GARCH model
