Using news analytics data in GARCH models

Sergei Sidorov (), Paresh Date () and Vladimir Balash ()
Additional contact information
Sergei Sidorov: Saratov State University, Russia
Paresh Date: Brunel University, London
Vladimir Balash: Saratov State University, Russia
Authors registered in the RePEc Author Service: Владимир Алексеевич Балаш

Applied Econometrics, 2013, vol. 29, issue 1, 82-96

Abstract: In this paper we analyze the impact of extraneous sources of information (viz. news and trade volume) on stock volatility by considering some augmented GARCH models. We suppose that trading volume can be considered as a proportional proxy for information arrivals to the market. Then we will consider the daily number of press releases on a stock (news intensity) as an alternative explanatory variable in the basic equation of GARCH model. We will show that the GARCH(1,1) model augmented with volume does remove GARCH and ARCH effects for the most of the companies, while the GARCH(1,1) model augmented with news intensity has difficulties in removing the impact of log return on volatility.

Keywords: stock volatility modeling; GARCH models (search for similar items in EconPapers)
JEL-codes: C32 C58 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:ris:apltrx:0204

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