IDEAS home Printed from https://ideas.repec.org/a/taf/quantf/v19y2019i9p1531-1542.html
   My bibliography  Save this article

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

Author

Listed:
  • Germán G. Creamer
  • Chihoon Lee

Abstract

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.

Suggested Citation

  • Germán G. Creamer & Chihoon Lee, 2019. "A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1531-1542, September.
  • Handle: RePEc:taf:quantf:v:19:y:2019:i:9:p:1531-1542
    DOI: 10.1080/14697688.2019.1622300
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/14697688.2019.1622300
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/14697688.2019.1622300?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.

    Citations

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


    Cited by:

    1. Antonis A. Michis, 2023. "Precious Metals Comovements in Turbulent Times: COVID-19 and the Ukrainian Conflict," JRFM, MDPI, vol. 16(5), pages 1-18, May.

    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:quantf:v:19:y:2019:i:9:p:1531-1542. 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/RQUF20 .

    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.