IDEAS home Printed from https://ideas.repec.org/a/eee/finana/v100y2025ics1057521925000705.html
   My bibliography  Save this article

Multivariate range-based EGARCH models

Author

Listed:
  • Yan, Lili
  • Kellard, Neil M.
  • Lambercy, Lyudmyla

Abstract

The dynamic conditional correlation (DCC) and co-range models are two main frameworks used to incorporate range-based univariate volatility. Using the two approaches, we construct novel multivariate range-based EGARCH (REGARCH) models: a DCC-REGARCH and co-range REGARCH (CRREGARCH) model, and a co-range CARR (CRCARR) model. We compare these models with five existing models over twelve forecast horizons, ranging from one to twelve weeks, covering currencies and ETFs. Among the eight models, the DCC-REGARCH and CRREGARCH models show the best performance in out-of-sample forecasting of the variance-covariance matrix across a range of market conditions and forecast horizons. These models also generate the lowest variance and turnover for global minimum-variance (GMV) portfolios in the majority of cases.

Suggested Citation

  • Yan, Lili & Kellard, Neil M. & Lambercy, Lyudmyla, 2025. "Multivariate range-based EGARCH models," International Review of Financial Analysis, Elsevier, vol. 100(C).
    • Handle: RePEc:eee:finana:v:100:y:2025:i:c:s1057521925000705
    DOI: 10.1016/j.irfa.2025.103983
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1057521925000705
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.irfa.2025.103983?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

    Keywords

    Range-based covariance forecasting; EGARCH; DCC; EWMA; Portfolio modelling;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

    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:eee:finana:v:100:y:2025:i:c:s1057521925000705. 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.elsevier.com/locate/inca/620166 .

    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.