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Public information arrival and volatility persistence in financial markets

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  • Gust Janssen

Abstract

This paper explores the relationship between daily market volatility and the arrival of public information in four different financial markets. Public information is measured as the daily number of economic news headlines, divided in six categories of news. Statistical analysis of the news data suggests the presence of particular seasonality effects, as well as a strong degree of autocorrelation. Over the period 1994-1998, significant effects of specific news categories on the volatility of US stocks, treasury bills, bonds and dollar were detected. However, the effects - in size and duration - vary by news category and by financial market. It is demonstrated that most of the volatility persistence, as observed by GARCH models, tends to disappear when news is included in the conditional variance equation.

Suggested Citation

  • Gust Janssen, 2004. "Public information arrival and volatility persistence in financial markets," The European Journal of Finance, Taylor & Francis Journals, vol. 10(3), pages 177-197.
    • Handle: RePEc:taf:eurjfi:v:10:y:2004:i:3:p:177-197
    DOI: 10.1080/1351847022000015812
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    References listed on IDEAS

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    1. Berry, Thomas D & Howe, Keith M, 1994. "Public Information Arrival," Journal of Finance, American Finance Association, vol. 49(4), pages 1331-1346, September.
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    Cited by:

    1. Kin‐Yip Ho & Lin Zheng & Zhaoyong Zhang, 2012. "Volume, volatility and information linkages in the stock and option markets," Review of Financial Economics, John Wiley & Sons, vol. 21(4), pages 168-174, November.
    2. Sidorov, Sergei & Date, Paresh & Balash, Vladimir, 2013. "Using news analytics data in GARCH models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 29(1), pages 82-96.
    3. Jianxin Wang, 2007. "Foreign Ownership and Volatility Dynamics of Indonesian Stocks," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 14(3), pages 201-210, September.
    4. Massimiliano Caporin & Francesco Poli, 2017. "Building News Measures from Textual Data and an Application to Volatility Forecasting," Econometrics, MDPI, vol. 5(3), pages 1-46, August.
    5. Mun, Melissa & Brooks, Robert, 2012. "The roles of news and volatility in stock market correlations during the global financial crisis," Emerging Markets Review, Elsevier, vol. 13(1), pages 1-7.
    6. Smales, L.A., 2021. "Macroeconomic news and treasury futures return volatility: Do treasury auctions matter?," Global Finance Journal, Elsevier, vol. 48(C).
    7. Ahmed, Mohamed Shaker & El-Masry, Ahmed A. & Al-Maghyereh, Aktham I. & Kumar, Satish, 2024. "Cryptocurrency volatility: A review, synthesis, and research agenda," Research in International Business and Finance, Elsevier, vol. 71(C).
    8. Soultanaeva, Albina, 2008. "Impact of Political News on the Baltic State Stock Markets," Umeå Economic Studies 735, Umeå University, Department of Economics.

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