IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v49y2017i52p5352-5369.html
   My bibliography  Save this article

Non-linear dependence modelling with bivariate copulas: statistical arbitrage pairs trading on the S&P 100

Author

Listed:
  • Christopher Krauss
  • Johannes Stübinger

Abstract

We develop a copula-based pairs trading framework and apply it to the S&P 100 index constituents from 1990 to 2014. We propose an integrated approach, relying on copulas for pairs selection and trading. Essentially, we fit t-copulas to all possible combinations of pairs in a formation period. Next, we trade these pairs in-sample to assess the profitability of mispricing signals derived from t-copulas. The top pairs are transferred to an out-of-sample trading period, and traded with individualized exit thresholds. In particular, we differentiate between pairs exhibiting mean-reversion and momentum effects and apply idiosyncratic take-profit and stop-loss rules. For the top 5 mean-reversion pairs, we find out-of-sample returns of 7.98% per year; the top 5 momentum pairs yield 7.22% per year. Standard deviations are low, leading to annualized Sharpe ratios of 1.52 (top 5 mean-reversion) and 1.33 (top 5 momentum), respectively.

Suggested Citation

  • Christopher Krauss & Johannes Stübinger, 2017. "Non-linear dependence modelling with bivariate copulas: statistical arbitrage pairs trading on the S&P 100," Applied Economics, Taylor & Francis Journals, vol. 49(52), pages 5352-5369, November.
    • Handle: RePEc:taf:applec:v:49:y:2017:i:52:p:5352-5369
    DOI: 10.1080/00036846.2017.1305097
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00036846.2017.1305097
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00036846.2017.1305097?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Matthew Clegg & Christopher Krauss, 2018. "Pairs trading with partial cointegration," Quantitative Finance, Taylor & Francis Journals, vol. 18(1), pages 121-138, January.
    2. Kasper Johansson & Thomas Schmelzer & Stephen Boyd, 2024. "Finding Moving-Band Statistical Arbitrages via Convex-Concave Optimization," Papers 2402.08108, arXiv.org.
    3. 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.
    4. 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.
    5. 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.
    6. Sabino da Silva, Fernando A.B. & Ziegelmann, Flavio A. & Caldeira, João F., 2023. "A pairs trading strategy based on mixed copulas," The Quarterly Review of Economics and Finance, Elsevier, vol. 87(C), pages 16-34.

    More about this item

    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:taf:applec:v:49:y:2017:i:52:p:5352-5369. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEC20 .

    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.