IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6463-054-1_32.html

Forecasting the Yield Curve with Nelson-Siegel Model: Chinese Evidence

In: Proceedings of the 2022 2nd International Conference on Financial Management and Economic Transition (FMET 2022)

Author

Listed:
  • Zhanyi Zhang

    (Yunnan University, School of Economics)

Abstract

The Dynamic Nelson-Siegel model is commonly used to forecast interest rate curves, but the way its parameters are estimated has so far been an issue worth investigating. In this paper, the performance of state space model is compared with that of two-step method in estimating parameters. Based on Chinese government bond data, this paper explores whether state space model has higher forecasting progress and better forecasting performance.

Suggested Citation

  • Zhanyi Zhang, 2023. "Forecasting the Yield Curve with Nelson-Siegel Model: Chinese Evidence," Advances in Economics, Business and Management Research, in: Vilas Gaikar & Min Hou & Sikandar Ali Qalati (ed.), Proceedings of the 2022 2nd International Conference on Financial Management and Economic Transition (FMET 2022), pages 283-290, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-054-1_32
    DOI: 10.2991/978-94-6463-054-1_32
    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:978-94-6463-054-1_32. 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.