IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v32y2013i2p111-123.html
   My bibliography  Save this article

Modeling and Forecasting the Yield Curve by an Extended Nelson‐Siegel Class of Models: A Quantile Autoregression Approach

Author

Listed:
  • Rafael B. Rezende
  • Mauro S. Ferreira

Abstract

No abstract is available for this item.

Suggested Citation

  • Rafael B. Rezende & Mauro S. Ferreira, 2013. "Modeling and Forecasting the Yield Curve by an Extended Nelson‐Siegel Class of Models: A Quantile Autoregression Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(2), pages 111-123, March.
    • Handle: RePEc:wly:jforec:v:32:y:2013:i:2:p:111-123
    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. Zi‐Yi Guo, 2021. "Out‐of‐sample performance of bias‐corrected estimators for diffusion processes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 243-268, March.
    2. Lorenčič Eva, 2016. "Testing the Performance of Cubic Splines and Nelson-Siegel Model for Estimating the Zero-coupon Yield Curve," Naše gospodarstvo/Our economy, Sciendo, vol. 62(2), pages 42-50, June.
    3. Daniel Vela, 2013. "Forecasting Latin-American yield curves: An artificial neural network approach," Borradores de Economia 10502, Banco de la Republica.
    4. Hokuto Ishii, 2019. "Forecasting Term Structure of Interest Rates in Japan," IJFS, MDPI, vol. 7(3), pages 1-35, July.
    5. Won Joong Kim & Gunho Jung & Sun-Yong Choi, 2020. "Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning," Complexity, Hindawi, vol. 2020, pages 1-23, July.
    6. Daniel Vela, 2013. "Forecasting Latin-American yield curves: An artificial neural network approach," Borradores de Economia 761, Banco de la Republica de Colombia.
    7. Tu, Anthony H. & Chen, Cathy Yi-Hsuan, 2018. "A factor-based approach of bond portfolio value-at-risk: The informational roles of macroeconomic and financial stress factors," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 243-268.
    8. Oleksandr Castello & Marina Resta, 2023. "A Machine-Learning-Based Approach for Natural Gas Futures Curve Modeling," Energies, MDPI, vol. 16(12), pages 1-22, June.
    9. João Caldeira & Guilherme Moura & André Santos, 2015. "Measuring Risk in Fixed Income Portfolios using Yield Curve Models," Computational Economics, Springer;Society for Computational Economics, vol. 46(1), pages 65-82, June.
    10. Tu, Anthony H. & Chen, Cathy Yi-Hsuan, 2016. "What derives the bond portfolio value-at-risk: Information roles of macroeconomic and financial stress factors," SFB 649 Discussion Papers 2016-006, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    11. Makram El-Shagi & Lunan Jiang, 2019. "Efficient Dynamic Yield Curve Estimation in Emerging Financial Markets," CFDS Discussion Paper Series 2019/4, Center for Financial Development and Stability at Henan University, Kaifeng, Henan, China.
    12. Hokuto Ishii, 2018. "Modeling and Predictability of Exchange Rate Changes by the Extended Relative Nelson–Siegel Class of Models," IJFS, MDPI, vol. 6(3), pages 1-15, August.
    13. repec:hum:wpaper:sfb649dp2016-006 is not listed on IDEAS
    14. Julián Andrada-Félix & Adrian Fernandez-Perez & Fernando Fernández-Rodríguez, 2015. "Fixed income strategies based on the prediction of parameters in the NS model for the Spanish public debt market," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 6(2), pages 207-245, June.
    15. Oleksandr Castello & Marina Resta, 2022. "Modeling the Yield Curve of BRICS Countries: Parametric vs. Machine Learning Techniques," Risks, MDPI, vol. 10(2), pages 1-18, February.

    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:32:y:2013:i:2:p:111-123. 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.