IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6463-758-8_188.html

Risk Diversification Portfolio Construction Using Discrete Hilbert Transform

In: Proceedings of the 9th International Conference on Accounting, Management, and Economics 2024 (ICAME 2024)

Author

Listed:
  • Wahyuni Ekasasmita

    (Institut Teknologi B.J. Habibie)

  • Muh. Rifki Nisardi

    (Institut Teknologi B.J. Habibie)

  • Nur Rahmi

    (Institut Teknologi B.J. Habibie)

  • Kusnaeni Kusnaeni

    (Institut Teknologi B.J. Habibie)

  • Hartina Husain

    (Institut Teknologi B.J. Habibie)

Abstract

This paper introduces the Discrete Hilbert Transform (DHT) as a tool for enhancing risk diversification in portfolio construction. DHT, a mathematical technique typically used in signal processing, is applied to financial data to identify dynamic market signals. By analyzing asset returns through the frequency domain, DHT enables investors to detect hidden risks that traditional models might overlook. This research compares portfolios constructed using DHT with those optimized via the conventional Mean-Variance method. The findings demonstrate that DHT-based portfolios provide better risk-adjusted returns, reduced volatility, and improved resilience during market fluctuations. The use of DHT allows for more adaptive and responsive portfolio adjustments, particularly in volatile conditions. This study contributes to the growing body of literature on applying advanced mathematical models to finance, offering practical insights for portfolio managers seeking to optimize risk diversification.

Suggested Citation

  • Wahyuni Ekasasmita & Muh. Rifki Nisardi & Nur Rahmi & Kusnaeni Kusnaeni & Hartina Husain, 2025. "Risk Diversification Portfolio Construction Using Discrete Hilbert Transform," Advances in Economics, Business and Management Research, in: Mursalim Nohong & Fitra Roman Cahaya & Phung Minh Tuan & Arifuddin Mannan & Anas Iswanto Anwar & Ria (ed.), Proceedings of the 9th International Conference on Accounting, Management, and Economics 2024 (ICAME 2024), pages 2359-2370, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-758-8_188
    DOI: 10.2991/978-94-6463-758-8_188
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    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:spr:advbcp:978-94-6463-758-8_188. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.