IDEAS home Printed from https://ideas.repec.org/a/taf/uteexx/v65y2020i2p114-134.html
   My bibliography  Save this article

Crypto-assets portfolio optimization under the omega measure

Author

Listed:
  • Javier Gutiérrez Castro
  • Edison Américo Huarsaya Tito
  • Luiz Eduardo Teixeira Brandão
  • Leonardo Lima Gomes

Abstract

Crypto-currencies, or crypto-assets, represent a new class of investment assets. The traditional portfolio analysis approach of Markowitz is not appropriate for use with portfolios containing crypto-assets, as the model requires that the investor have a quadratic utility function or that the returns be normally distributed, which isn’t the case for crypto-assets. We develop a portfolio optimization model based on the Omega measure which is more comprehensive than the Markowitz model, and apply this to four crypto-asset investment portfolios by means of a numerical application. The results indicate that these portfolios should favor traditional market assets over crypto-assets. In the case of portfolios formed only by crypto-assets, there is no clear preference in favor of any crypto-asset in particular.

Suggested Citation

  • Javier Gutiérrez Castro & Edison Américo Huarsaya Tito & Luiz Eduardo Teixeira Brandão & Leonardo Lima Gomes, 2020. "Crypto-assets portfolio optimization under the omega measure," The Engineering Economist, Taylor & Francis Journals, vol. 65(2), pages 114-134, April.
    • Handle: RePEc:taf:uteexx:v:65:y:2020:i:2:p:114-134
    DOI: 10.1080/0013791X.2019.1668098
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/0013791X.2019.1668098
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/0013791X.2019.1668098?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.

    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:uteexx:v:65:y:2020:i:2:p:114-134. 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/UTEE20 .

    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.