IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-04875463.html
   My bibliography  Save this paper

Pricing of European currency options considering the dynamic information costs

Author

Listed:
  • Wael Dammak

    (LSAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon, UCBL - Université Claude Bernard Lyon 1 - Université de Lyon)

  • Salah Ben Hamad

    (Université de Sfax - University of Sfax)

  • Christian de Peretti

    (ECL - École Centrale de Lyon - Université de Lyon, LSAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon)

  • Hichem Eleuch

    (INSAT - Institut National des Sciences Appliquées et de Technologie [Tunis])

Abstract

Dynamic costs arising from the variable impact of information on asset pricing present a challenge for accurate European currency option pricing. The Garman and Kohlhagen model, though influential in the literature, does not adequately account for these costs. This study extends the model by integrating an intensity function into the interest rates to measure dynamic information costs. Inspired by the Beer–Lambert law, the function is applied to a decade-long dataset of daily futures continuous calls on the EUR/USD pair from September 21, 2012, to September 23, 2022. The augmented model reduces pricing errors and manages implied volatility better than the 1983 model, consistent across different categories of maturity and moneyness. Our findings emphasize the need to consider dynamic information costs in asset pricing, demonstrating that their inclusion can significantly enhance the accuracy and reliability of currency option pricing.

Suggested Citation

  • Wael Dammak & Salah Ben Hamad & Christian de Peretti & Hichem Eleuch, 2023. "Pricing of European currency options considering the dynamic information costs," Post-Print hal-04875463, HAL.
  • Handle: RePEc:hal:journl:hal-04875463
    DOI: 10.1016/j.gfj.2023.100897
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

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


    Cited by:

    1. Ali Trabelsi Karoui & Sonia Sayari & Wael Dammak & Ahmed Jeribi, 2024. "Unveiling Outperformance: A Portfolio Analysis of Top AI-Related Stocks against IT Indices and Robotics ETFs," Risks, MDPI, vol. 12(3), pages 1-21, March.

    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:hal:journl:hal-04875463. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.