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Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2019 002

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

Ya Qian
Jun Tu
Wolfgang Karl Härdle

Abstract:
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.

Keywords:
information arrival, volatility modeling, jump, sentiment, GARCH

JEL Classification:
C52, C55, C58, G14