IDEAS home Printed from https://ideas.repec.org/a/eee/ecmode/v152y2025ics0264999325002299.html
   My bibliography  Save this article

Systematic index option-writing strategies with Black-Scholes-Merton and Variance-Gamma Models

Author

Listed:
  • Wysocki, Maciej
  • Ślepaczuk, Robert

Abstract

This study evaluates systematic S&P 500 Index option-writing strategies, comparing the hedging performance of the Black–Scholes-Merton (BSM) and Variance-Gamma (VG) models, bridging the gap between theoretical models and their practical applications in trading. Using 1-minute data from 2018 to 2023, we assess hedged and unhedged strategies against buy-and-hold benchmarks, incorporating transaction costs to validate different hedging and sizing methodologies. Our findings reveal that systematic option writing can generate superior risk-adjusted returns. The BSM model generally outperforms the VG model in hedging, leveraging implied volatility to reflect market conditions accurately. However, the VG model proves valuable for position sizing in certain naked strategies, capturing skewness and kurtosis to manage tail risks. Intraday hedging at 130 min intervals offers effective downside protection while preserving return potential. The insights on hedging and sizing presented in this study provide actionable guidance for institutional and non-institutional participants in options markets.

Suggested Citation

  • Wysocki, Maciej & Ślepaczuk, Robert, 2025. "Systematic index option-writing strategies with Black-Scholes-Merton and Variance-Gamma Models," Economic Modelling, Elsevier, vol. 152(C).
  • Handle: RePEc:eee:ecmode:v:152:y:2025:i:c:s0264999325002299
    DOI: 10.1016/j.econmod.2025.107234
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0264999325002299
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.econmod.2025.107234?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

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    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:eee:ecmode:v:152:y:2025:i:c:s0264999325002299. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30411 .

    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.