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Statistical Arbitrage Pairs Trading with High-frequency Data

Author

Listed:
  • Johannes St binger

    (Department of Statistics and Econometrics, University of Erlangen-N rnberg, Lange Gasse 20, 90403 N rnberg, Germany,)

  • Jens Bredthauer

    (Department of Statistics and Econometrics, University of Erlangen-N rnberg, Lange Gasse 20, 90403 N rnberg, Germany,)

Abstract

In recent years, more sophisticated techniques for analyzing data and exponential increase in computing power allow high-frequency trading. This paper provides a detailed overview on pairs trading in the context of intraday data and applies different strategies to minute-by-minute prices of the S&P 500 constituents from 1998 to 2015. In the back-testing study, the best performing pairs trading approach produces statistically and economically significant returns of 50.50% p.a. and an annualized Sharpe ratio of 8.14 after transaction costs. Although most algorithms show declining returns over time, there still exist pairs trading strategies with favorable results in the recent past.

Suggested Citation

  • Johannes St binger & Jens Bredthauer, 2017. "Statistical Arbitrage Pairs Trading with High-frequency Data," International Journal of Economics and Financial Issues, Econjournals, vol. 7(4), pages 650-662.
    • Handle: RePEc:eco:journ1:2017-04-76
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Adam Korniejczuk & Robert Ślepaczuk, 2024. "Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market," Working Papers 2024-09, Faculty of Economic Sciences, University of Warsaw.
    2. Endres, Sylvia & Stübinger, Johannes, 2017. "Optimal trading strategies for Lévy-driven Ornstein-Uhlenbeck processes," FAU Discussion Papers in Economics 17/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    3. Poutré, Cédric & Dionne, Georges & Yergeau, Gabriel, 2023. "International high-frequency arbitrage for cross-listed stocks," International Review of Financial Analysis, Elsevier, vol. 89(C).
    4. Johannes Stübinger & Lucas Schneider, 2019. "Statistical Arbitrage with Mean-Reverting Overnight Price Gaps on High-Frequency Data of the S&P 500," JRFM, MDPI, vol. 12(2), pages 1-19, April.
    5. Endres, Sylvia & Stübinger, Johannes, 2018. "A flexible regime switching model with pairs trading application to the S&P 500 high-frequency stock returns," FAU Discussion Papers in Economics 07/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    6. Johannes Stübinger & Sylvia Endres, 2018. "Pairs trading with a mean-reverting jump–diffusion model on high-frequency data," Quantitative Finance, Taylor & Francis Journals, vol. 18(10), pages 1735-1751, October.
    7. Stübinger, Johannes, 2018. "Statistical arbitrage with optimal causal paths on high-frequencydata of the S&P 500," FAU Discussion Papers in Economics 01/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    8. Stübinger, Johannes & Walter, Dominik & Knoll, Julian, 2017. "Financial market predictions with Factorization Machines: Trading the opening hour based on overnight social media data," FAU Discussion Papers in Economics 19/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.

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    More about this item

    Keywords

    Finance; Pairs Trading; High-frequency data;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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