IDEAS home Printed from https://ideas.repec.org/a/fzy/fuzeco/vxviy2011i2p59-73.html
   My bibliography  Save this article

Pricing Brazilian exchange rate options using an adaptive network-based fuzzy inference system

Author

Listed:
  • Maciel, Leandro S.

    (University of Campinas)

Abstract

Recently, option pricing has become the focus of risk managers, policymakers, traders and more generally all market participants, since they find valuable information in these contracts. This paper suggests the pricing performance evaluation on Brazilian exchange rate R$ (Reais) per US$ (U.S. Dollar) option contracts, traded at the Brazilian derivatives market, using an adaptive network-based fuzzy inference system, for the period from April 1999 to April 2009. A fuzzy rule-based system was built with a family of conditional if-then statements whose consequent are functions of the antecedents, and then composed with the aid of fuzzy neurons. The ANFIS model was compared against the Black closed-form formula and some neural networks topologies, considering traditional error measures and statistical tests. The results showed that the ANFIS model outperforms closed-form formula methodology in pricing Brazilian currency options, mainly for out-of-the money contracts.

Suggested Citation

  • Maciel, Leandro S., 2011. "Pricing Brazilian exchange rate options using an adaptive network-based fuzzy inference system," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 0(2), pages 59-73, November.
    • Handle: RePEc:fzy:fuzeco:v:xvi:y:2011:i:2:p:59-73
    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.

    More about this item

    Keywords

    ANFIS; option pricing; derivatives; exchange rate; prediction;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:fzy:fuzeco:v:xvi:y:2011:i:2:p:59-73. 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: Aurelio Fernandez (email available below). General contact details of provider: https://edirc.repec.org/data/sigefea.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.