IDEAS home Printed from https://ideas.repec.org/p/pri/econom/2022-25.html
   My bibliography  Save this paper

Nonparametric Option Pricing with Generalized Entropic Estimators

Author

Listed:
  • Caio Almeida

    (Princeton University)

  • Gustavo Freire

    (Erasmus School of Economics)

  • Rafael Azevedo

    (Getulio Vargas Foundation (FGV))

  • Kym Ardison

    (Getulio Vargas Foundation (FGV))

Abstract

We propose a family of nonparametric estimators for an option price that require only the use of underlying return data, but can also easily incorporate information from observed option prices. Each estimator comes from a risk-neutral measure minimizing generalized entropy according to a different Cressie-Read discrepancy. We apply our method to price S&P 500 options and the cross-section of individual equity options, using distinct amounts of option data in the estimation. Estimators incorporating mild nonlinearities produce optimal pricing accuracy within the Cressie-Read family and outperform several benchmarks such as the Black-Scholes and different GARCH option pricing models. Overall, we provide a powerful option pricing technique suitable for scenarios of limited option data availability.

Suggested Citation

  • Caio Almeida & Gustavo Freire & Rafael Azevedo & Kym Ardison, 2022. "Nonparametric Option Pricing with Generalized Entropic Estimators," Working Papers 2022-25, Princeton University. Economics Department..
    • Handle: RePEc:pri:econom:2022-25
    as

    Download full text from publisher

    File URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2535790
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Risk-Neutral Measure; Option Pricing; Nonparametric Estimation; Generalized Entropy; Cressie-Read Discrepancies;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pri:econom:2022-25. 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: Bobray Bordelon (email available below). General contact details of provider: https://economics.princeton.edu/working-papers/ .

    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.