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New Intensity and Conditional Volatility on the French Stock Market

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  • Jean-Gabriel Cousin
  • Tanguy de Launois

Abstract

The relation between information flow and asset prices behavior is one of the key issues of modern finance. Our study investigates more closely the link between frequency of information arrivals and stock return volatility. It aims precisely to test empirically the mixture of distribution hypothesis and to check whether the stock returns distribution is driven by the frequencies of information arrivals on the Paris stock Exchange (Euronext). We analyze the impact of news on volatility at the firm-level. We opt for a model with two (Markov switching) regimes of volatility that we apply to all stocks pertaining to the CAC40 index from January 1999 to December 2003. We find a positive and significant but marginally decreasing impact of the daily frequency of information arrivals on the probability to be in a state of high volatility for each of the 40 companies considered. The subsequent model for panel data allows us to conclude that this impact crucially depends on the timing and the topic of the news release. Asymmetry and informational content issues are also investigated. Results are consistent with previous literature, although we show that any asymmetric effect disappears once the news informational content is accounted for.

Suggested Citation

  • Jean-Gabriel Cousin & Tanguy de Launois, 2006. "New Intensity and Conditional Volatility on the French Stock Market," Finance, Presses universitaires de Grenoble, vol. 27(1), pages 7-60.
    • Handle: RePEc:cai:finpug:fina_271_0007
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    Cited by:

    1. Uctum, Remzi & Renou-Maissant, Patricia & Prat, Georges & Lecarpentier-Moyal, Sylvie, 2017. "Persistence of announcement effects on the intraday volatility of stock returns: Evidence from individual data," Review of Financial Economics, Elsevier, vol. 35(C), pages 43-56.
    2. Nidhal Mgadmi & Khemaies Bougatef, 2017. "Modeling volatility of the French stock market," Economics Bulletin, AccessEcon, vol. 37(2), pages 988-998.
    3. Alpha Basweti Kenyatta & Antony Ngunyi & Anthony Gichuhi Waititu, 2020. "News Classification using Support Vector Machine to Model and Forecast Volatility," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 9(1), pages 1-1.

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