IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v31y2012i6p540-564.html
   My bibliography  Save this article

Nelson–Siegel, Affine and Quadratic Yield Curve Specifications: Which One is Better at Forecasting?

Author

Listed:
  • Ken Nyholm
  • Rositsa Vidova‐Koleva

Abstract

No abstract is available for this item.

Suggested Citation

  • Ken Nyholm & Rositsa Vidova‐Koleva, 2012. "Nelson–Siegel, Affine and Quadratic Yield Curve Specifications: Which One is Better at Forecasting?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(6), pages 540-564, September.
  • Handle: RePEc:wly:jforec:v:31:y:2012:i:6:p:540-564
    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 search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Albert K. Tsui & Junxiang Wu & Zhaoyong Zhang & Zhongxi Zheng, 2023. "Forecasting term structure of the Japanese bond yields in the presence of a liquidity trap," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1205-1227, August.
    2. Laurini, Márcio P. & Caldeira, João F., 2016. "A macro-finance term structure model with multivariate stochastic volatility," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 68-90.
    3. João F. Caldeira & Guilherme V. Moura & , Fabricio Tourrucôo, 2016. "Forecasting the yield curve with the arbitrage-free dynamic Nelson-Siegel model: Brazilian evidence," Economia, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics], vol. 17(2), pages 221-237.
    4. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Predicting the yield curve using forecast combinations," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 79-98.
    5. Giuseppe Arbia & Michele Di Marcantonio, 2015. "Forecasting Interest Rates Using Geostatistical Techniques," Econometrics, MDPI, vol. 3(4), pages 1-28, November.
    6. Kentaro Kikuchi, 2024. "A term structure interest rate model with the Brownian bridge lower bound," Annals of Finance, Springer, vol. 20(3), pages 301-328, September.
    7. Kladívko, Kamil & Rusý, Tomáš, 2023. "Maximum likelihood estimation of the Hull–White model," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 227-247.

    More about this item

    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:wly:jforec:v:31:y:2012:i:6:p:540-564. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    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.