IDEAS home Printed from https://ideas.repec.org/a/bpj/jtsmet/v17y2025i1p35-67n1001.html
   My bibliography  Save this article

Forecasting High-Dimensional Portfolios

Author

Listed:
  • Mattera Raffaele

    (Department of Mathematics and Physics, University of Campania “Luigi Vanvitelli”, Caserta, Italy)

Abstract

In this paper, we investigate the usefulness of forecasting in a high-dimensional framework where the number of assets is larger than the temporal observations. The benefit of forecasting lies in the concept of timing, which means anticipating future market conditions. We find that when high-dimensional econometric approaches are used, forecasting either the mean or the covariance is better than predicting both and then approaches based on static estimates. Moreover, we find that timing portfolios also perform better than the naive strategy. Considering the portfolio returns over time, we find that a possible explanation for the better performance of volatility-timing portfolios is that they better manage risk during periods of high uncertainty.

Suggested Citation

  • Mattera Raffaele, 2025. "Forecasting High-Dimensional Portfolios," Journal of Time Series Econometrics, De Gruyter, vol. 17(1), pages 35-67.
    • Handle: RePEc:bpj:jtsmet:v:17:y:2025:i:1:p:35-67:n:1001
    DOI: 10.1515/jtse-2023-0011
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jtse-2023-0011
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/jtse-2023-0011?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

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    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:bpj:jtsmet:v:17:y:2025:i:1:p:35-67:n:1001. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyterbrill.com .

    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.