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High Frequency Lead/lag Relationships - Empirical facts

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Author Info

  • Nicolas Huth

    ()
    (MAS - Mathématiques Appliquées aux Systèmes - EA 4037 - Ecole Centrale Paris, FiQuant - Chaire de finance quantitative - Ecole Centrale Paris)

  • Frédéric Abergel

    (MAS - Mathématiques Appliquées aux Systèmes - EA 4037 - Ecole Centrale Paris, FiQuant - Chaire de finance quantitative - Ecole Centrale Paris)

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    Abstract

    Lead/lag relationships are an important stylized fact at high frequency. Some assets follow the path of others with a small time lag. We provide indicators to measure this phenomenon using tick-by-tick data. Strongly asymmetric cross-correlation functions are empirically observed, especially in the future/stock case. We confirm the intuition that the most liquid assets (short intertrade duration, narrow bid/ask spread, small volatility, high turnover) tend to lead smaller stocks. However, the most correlated stocks are those with similar levels of liquidity. This lead/lag phenomenon is not constant throughout the day, it shows an intraday seasonality with changes of behaviour at very specific times such as the announcement of macroeconomic figures and the US market opening. These lead/lag relationships become more and more pronounced as we zoom on significant events. We reach 60% of accuracy when forecasting the next midquote variation of the lagger using only the past information of the leader, which is significantly better than using the information of the lagger only. However, a naive strategy based on market orders cannot make any profit of this effect because of the bid/ask spread.

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    Bibliographic Info

    Paper provided by HAL in its series Working Papers with number hal-00645685.

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    Date of creation: 28 Nov 2011
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    Handle: RePEc:hal:wpaper:hal-00645685

    Note: View the original document on HAL open archive server: http://hal.archives-ouvertes.fr/hal-00645685/en/
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    Related research

    Keywords: Lead/lag; correlation; high frequency data; market microstructure;

    References

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    1. Mech, Timothy S., 1993. "Portfolio return autocorrelation," Journal of Financial Economics, Elsevier, vol. 34(3), pages 307-344, December.
    2. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-38, July.
    3. Griffin, Jim E. & Oomen, Roel C.A., 2011. "Covariance measurement in the presence of non-synchronous trading and market microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 58-68, January.
    4. Zhang, Lan, 2011. "Estimating covariation: Epps effect, microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 33-47, January.
    5. Kadlec, Gregory B & Patterson, Douglas M, 1999. "A Transactions Data Analysis of Nonsynchronous Trading," Review of Financial Studies, Society for Financial Studies, vol. 12(3), pages 609-30.
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