Information flows and stock market volatility

Chew Lian Chua, Sarantis Tsiaplias

Research output: Journal PublicationArticlepeer-review

11 Citations (Scopus)


This study examines how news is distributed across stocks. A model is developed that categorizes a stock's latent news into normal and nonnormal news, and allows both types of news to be filtered through to other stocks. This is achieved by formulating a model that jointly incorporates a multivariate lognormal-Poisson jump process (for nonnormal news) and a multivariate GARCH process (for normal news), in addition to a news (or shock) transmission mechanism that allows the shocks from both processes to impact intertemporally on all stocks in the system. The relationship between news and the expected volatility surface is explored and a unique news impact surface is derived that depends on time, news magnitude, and news type. We find that the effect of nonnormal news on volatility expectations typically builds up before dissipating, with the news transmission mechanism effectively crowding-out normal news and crowding-in nonnormal news. Moreover, in contrast to the standard approach for measuring leverage effects using asymmetric generalized autoregressive conditional heteroskedasticity models, we find that leverage effects stem predominantly from nonnormal news. Finally, we find that the capacity to identify positively or negatively correlated stock returns is ambiguous in the short term, and depends heavily on the behavior of the nonnormal news component.

Original languageEnglish
Pages (from-to)129-148
Number of pages20
JournalJournal of Applied Econometrics
Issue number1
Publication statusPublished - 1 Jan 2019

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Economics and Econometrics


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