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Stock Market Integration and the Speed of Information Transmission

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Using a unique dataset covering two years of high frequency data on the indices from markets in the U. S., London, Frankfurt, Paris, Warsaw, Prague, and Budapest, we perform cointegration and Granger causality tests with data of different frequencies (from 5 minutes to 1 day). The aim is to describe the time structure in which markets react to the information revealed in prices on other markets. The results suggest that the speed of information transmission is very fast. In all cases the strongest reaction occurs within 1 hour. Therefore, the use of daily data may be misleading when analyzing the issues of stock market integration and information transmission between markets.

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  • Alexandr Èerný & Michal Koblas, 2008. "Stock Market Integration and the Speed of Information Transmission," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 58(01-02), pages 2-20, January.
  • Handle: RePEc:fau:fauart:v:58:y:2008:i:1-2:p:2-20
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    11. Mukherjee, Dr. Kedar nath & Mishra, Dr. R. K., 2008. "Stock Market Integration and Volatility Spillover:India and its Major Asian Counterparts," MPRA Paper 12788, University Library of Munich, Germany.
    12. Dragan Tevdovski, 2014. "Extreme negative coexceedances in South Eastern European stock markets," CREATES Research Papers 2014-18, Department of Economics and Business Economics, Aarhus University.
    13. Horobet, Alexandra & Lupu, Radu, 2009. "Are Capital Markets Integrated? A Test of Information Transmission within the European Union," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 6(2), pages 64-80, June.
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    15. Ekaterina Dorodnykh, 2012. "What Is the Degree of Convergence among Developed Equity Markets?," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 3(2), pages 2-16, April.
    16. Pereira, Hernane Borges de Barros & Rosário, Raphael Silva do & Pereira, Eder Johnson de Area Leão & Moreira, Davidson Martins & Ferreira, Paulo & Miranda, José Garcia Vivas, 2022. "Network dynamic and stability on European Union," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    17. Horobet, Alexandra & Ilie, Livia, 2007. "On the dynamic link between stock prices and exchange rates: evidence from Romania," MPRA Paper 6429, University Library of Munich, Germany.
    18. Yusaku Nishimura & Yoshiro Tsutsui & Kenjiro Hirayama, 2012. "Return and Volatility Spillovers between Japanese and Chinese Stock Markets FAn Analysis of Overlapping Trading Hours with High-frequency Data," Discussion Papers in Economics and Business 12-01, Osaka University, Graduate School of Economics.
    19. Wojciech Grabowski, 2019. "Givers or Recipients? Co-Movements between Stock Markets of CEE-3 and Developed Countries," Sustainability, MDPI, vol. 11(22), pages 1-24, November.
    20. Radman Peša, Anita & Brajković, Ana, 2015. "Testing The ‘Black Swan Effect’ on Croatian Stock Market Between 2000 and 2013," MPRA Paper 69223, University Library of Munich, Germany, revised 2015.
    21. Ani Stoykova & Mariya Paskaleva, 2018. "Correlation Dynamics between Southeast European Capital Markets," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 4, pages 49-82.
    22. Eduard Baumöhl, 2014. "Determinanty integrácie akciových trhov krajín V4 [Determinants of CEE-4 Stock Market Integration]," Politická ekonomie, Prague University of Economics and Business, vol. 2014(3), pages 347-365.

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    More about this item

    Keywords

    stock market integration; market comovement; intra-day data; speed of information transmission; cointegration; Granger causality;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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