IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v649y2024ics0378437124004576.html
   My bibliography  Save this article

Hedging global currency risk: A dynamic machine learning approach

Author

Listed:
  • Pagnottoni, Paolo
  • Spelta, Alessandro

Abstract

We propose a dynamic method to hedge foreign exchange risk of international equity portfolios. The method is based on the currency return forecasts derived from a set of alternative machine learning models, built on the main factor components of the currency return time series. To illustrate our method, we take the perspective of a US portfolio manager investing in a global equity portfolio of developing and developed economies. The analysis of several model performance indicators, back-tested on data from 27 October 2008 to 30 December 2022, allows to conclude that accurate predictions of global currency factor returns, such as those obtained with non linear machine learning models, can improve currency risk hedging of global equity portfolios.

Suggested Citation

  • Pagnottoni, Paolo & Spelta, Alessandro, 2024. "Hedging global currency risk: A dynamic machine learning approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 649(C).
    • Handle: RePEc:eee:phsmap:v:649:y:2024:i:c:s0378437124004576
    DOI: 10.1016/j.physa.2024.129948
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437124004576
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2024.129948?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.

    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:phsmap:v:649:y:2024:i:c:s0378437124004576. 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.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.