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

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: IRTG 1792 Discussion Paper ; No. 2019-002

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Model Evaluation, Validation, and Selection ; Large Data Sets: Modeling and Analysis ; Financial Econometrics ; Information and Market Efficiency; Event Studies; Insider Trading
• Thema: information arrival ; volatility modeling ; jump ; sentiment ; GARCH

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Qian, Ya ; Tu, Jun ; Härdle, Wolfgang Karl
• Ereignis: Veröffentlichung
• (wer): Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
• (wo): Berlin
• (wann): 2019

Weitere Informationen

• Handle: http://hdl.handle.net/10419/230778
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ