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

This work aims to investigate the (inter)relations of information arrival, news sentiment, volatility and jump dynamics of intraday returns. Two parametric GARCHtype 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 behaviours, these two models are compared with several other widely used versions of GARCH-type models based on the calibration results on DJIA 30 stocks. The necessity to include news processes in intraday stock volatility modelling is justified in our specific calibration samples (2008 and 2013, respectively). However, our results reject higher profitability of separate jump process modelling compared to a simple GARCH process with error distribution capable of capturing fat tail behaviours of financial time series, what allows to avoid the complicatedness of modelling. Thus, our empirical results suggest GARCH-news model with skew-t innovation distribution as the best candidate for intraday returns of large stocks in the US market.