IDEAS home Printed from https://ideas.repec.org/p/war/wpaper/2025-19.html
   My bibliography  Save this paper

A survey of statistical arbitrage pairs trading strategies with non-machine learning methods, 2016-2023

Author

Listed:
  • Yufei Sun

    (Faculty of Economic Sciences, University of Warsaw)

Abstract

This review examines the growing literature on pairs trading frameworks, which involve relative value arbitrage strategies between two or more securities. Existing research is categorized into five main categories: distance methods use nonparametric distance measures to identify pairs trading opportunities; cointegration methods rely on formal cointegration tests to reveal stationary time series of spreads; time series methods focus on finding optimal trading rules for mean-reverting spreads; stochastic control methods aim to determine the optimal portfolio holdings in pairs trading relative to other available securities; and the "Other Methods" category encompasses other relevant pairs trading frameworks, albeit with a more limited supporting literature. Through a comprehensive review of over 100 papers published between 2016 and 2023, the survey identifies the key strengths and weaknesses of each approach, providing insights relevant for future research and practical implementation.

Suggested Citation

  • Yufei Sun, 2025. "A survey of statistical arbitrage pairs trading strategies with non-machine learning methods, 2016-2023," Working Papers 2025-19, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2025-19
    as

    Download full text from publisher

    File URL: https://www.wne.uw.edu.pl/download_file/6095/0
    File Function: First version, 2025
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:war:wpaper:2025-19. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Jacek Rapacz (email available below). General contact details of provider: https://edirc.repec.org/data/fesuwpl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.