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High Frequency Lead/lag Relationships - Empirical facts

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  • Nicolas Huth
  • Fr\'ed\'eric Abergel
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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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    File URL: http://arxiv.org/pdf/1111.7103
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    Bibliographic Info

    Paper provided by arXiv.org in its series Papers with number 1111.7103.

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    Date of creation: Nov 2011
    Date of revision: Jan 2012
    Handle: RePEc:arx:papers:1111.7103

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    Web page: http://arxiv.org/

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    1. Zhang, Lan, 2011. "Estimating covariation: Epps effect, microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 33-47, January.
    2. 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.
    3. 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.
    4. 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.
    5. Mech, Timothy S., 1993. "Portfolio return autocorrelation," Journal of Financial Economics, Elsevier, vol. 34(3), pages 307-344, December.
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