Information Arrival, News Sentiment, Volatilities and Jumps of Intraday Returns

This work aims to investigate the (inter)relations of information arrival, news sentiment, volatilities and jump dynamics of intraday returns. Two parametric GARCH-type jump models which explicitly incorporate both news arrival and news sentiment variables are proposed, among which one assumes news affecting financial markets through the jump component while the other postulating the GARCH component channel. In order to give the most-likely format of the interactions between news arrival and stock market behaviors, these two models are compared with several other easier versions of GARCH-type models based on the calibration results on DJIA 30 stocks. The necessity to include news processes in intraday stock volatility modeling is justified in our specific calibration samples (2008 and 2013, respectively). While it is not as profitable to model jump process separately as using simpler GARCH process with error distribution capable to capture fat tail behaviors of financial time series. In conclusion, our calibration results suggest GARCH-news model with skew-t innovation distribution as the best candidate for intraday returns of large stocks in US market, which means one can probably avoid the complicatedness of modelling jump behavior by using a simplier skew-t error distribution assumption instead, but it’s necessary to incorporate news variables.