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Information flows and stock market volatility

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  • Chew Lian Chua
  • Sarantis Tsiaplias

Abstract

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.

Suggested Citation

  • Chew Lian Chua & Sarantis Tsiaplias, 2019. "Information flows and stock market volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(1), pages 129-148, January.
    • Handle: RePEc:wly:japmet:v:34:y:2019:i:1:p:129-148
    DOI: 10.1002/jae.2649
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    Cited by:

    1. Shamrez Ali, Sundus Waqar, Muhammad Haris, 2019. "The Nexus between Political & Institutional Corruption Events with the Stock Market: A Study of Pakistan," Journal of Finance and Economics Research, Geist Science, Iqra University, Faculty of Business Administration, vol. 4(1), pages 59-71, March.
    2. Li, Chenxing & Maheu, John M, 2020. "A Multivariate GARCH-Jump Mixture Model," MPRA Paper 104770, University Library of Munich, Germany.
    3. Nappo, Giovanna & Marchetti, Fabio Massimo & Vagnani, Gianluca, 2023. "Traders’ heterogeneous beliefs about stock volatility and the implied volatility skew in financial options markets," Finance Research Letters, Elsevier, vol. 53(C).
    4. Wei Zhang & Kai Yan & Dehua Shen, 2021. "Can the Baidu Index predict realized volatility in the Chinese stock market?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-31, December.
    5. Niu, Zibo & Liu, Yuanyuan & Gao, Wang & Zhang, Hongwei, 2021. "The role of coronavirus news in the volatility forecasting of crude oil futures markets: Evidence from China," Resources Policy, Elsevier, vol. 73(C).

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