IDEAS home Printed from https://ideas.repec.org/a/eee/riibaf/v79y2025ics0275531925002922.html
   My bibliography  Save this article

Multi-objective carbon-energy portfolio optimization under investment horizon heterogeneity

Author

Listed:
  • Xue, Jianhao
  • Dai, Xingyu
  • Xiao, Ling
  • Wang, Qunwei
  • Li, Matthew C.

Abstract

European geopolitical risks and financial systemic risks have led to significant long-term and short-term fluctuations in global carbon and energy markets. This creates challenges for portfolio managers with different investment horizons. This study uses a multivariate variational mode decomposition (MVMD) based Vine-Copula model to perform multi-objective portfolio optimization. There are ten optimization objectives and different measures, including Mean, Variance, Skewness, Kurtosis, VaR, CVaR, and HMCR. This study analyzes carbon, natural gas, oil and coal futures assets data from December 31, 2009 to October 21, 2022, and finds the following. First, portfolios with investment horizons over 20 weeks- and less than 2 weeks-length outperform portfolios with other investment horizons. Second, the optimized Mean-Skewness-CVaR portfolio has the greatest number of optimal timings for portfolio managers at investment horizons over 20 weeks and less than 2 weeks. Portfolio performance does not necessarily improve by including more optimization objectives within portfolio strategies.

Suggested Citation

  • Xue, Jianhao & Dai, Xingyu & Xiao, Ling & Wang, Qunwei & Li, Matthew C., 2025. "Multi-objective carbon-energy portfolio optimization under investment horizon heterogeneity," Research in International Business and Finance, Elsevier, vol. 79(C).
    • Handle: RePEc:eee:riibaf:v:79:y:2025:i:c:s0275531925002922
    DOI: 10.1016/j.ribaf.2025.103036
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0275531925002922
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ribaf.2025.103036?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

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:riibaf:v:79:y:2025:i:c:s0275531925002922. 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/ribaf .

    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.