IDEAS home Printed from https://ideas.repec.org/a/rjr/romjef/vy2019i1p101-113.html
   My bibliography  Save this article

Forecasting the Yield Curve with Dynamic Factors

Author

Listed:
  • Erhard RESCHENHOFER

    (University of Vienna, Department of Statistics and Operations Research, Oskar-Morgenstern-Platz 1, 1090 Vienna, Austria.)

  • Thomas STARK

    (University of Vienna, Department of Statistics and Operations Research, Oskar-Morgenstern-Platz 1, 1090 Vienna, Austria)

Abstract

Using two monthly yield datasets over the periods 1970-2000 and 1990-2019, respectively, we re-examine previous findings that yield forecasts based on AR models for the dynamic factors obtained from the Nelson-Siegel curve outperform the random walk forecast and other competitors. Our empirical results do not support these findings. Only the forecasts based on AR models for the differenced yields outperform the random walk forecast. In general, the 1-month-ahead forecasts based on the dynamic factors come out worse than those based on the yields. In the case of 12-months-ahead forecasting, all forecasts perform poorly, particularly those based on AR models fitted to undifferenced time series. Seemingly more positive results obtained in previous studies are explained by a focus on a too short evaluation period.

Suggested Citation

  • Erhard RESCHENHOFER & Thomas STARK, 2019. "Forecasting the Yield Curve with Dynamic Factors," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 101-113, March.
    • Handle: RePEc:rjr:romjef:v::y:2019:i:1:p:101-113
    as

    Download full text from publisher

    File URL: http://www.ipe.ro/rjef/rjef1_19/rjef1_2019p101-113.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Christensen, Jens H.E. & Diebold, Francis X. & Rudebusch, Glenn D., 2011. "The affine arbitrage-free class of Nelson-Siegel term structure models," Journal of Econometrics, Elsevier, vol. 164(1), pages 4-20, September.
    2. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February.
    3. Chen, Ying & Niu, Linlin, 2014. "Adaptive dynamic Nelson–Siegel term structure model with applications," Journal of Econometrics, Elsevier, vol. 180(1), pages 98-115.
    4. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    5. Shang, Yuhuang & Zheng, Tingguo, 2018. "Fitting and forecasting yield curves with a mixed-frequency affine model: Evidence from China," Economic Modelling, Elsevier, vol. 68(C), pages 145-154.
    6. John H. Cochrane & Monika Piazzesi, 2005. "Bond Risk Premia," American Economic Review, American Economic Association, vol. 95(1), pages 138-160, March.
    7. Jacob Muvingi & Takudzwa Kwinjo, 2014. "Estimation of Term Structures using Nelson-Siegel and Nelson-Siegel-Svensson: A Case of a Zimbabwean Bank," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 4(6), pages 1-9.
    8. Lars E.O. Svensson, 1994. "Estimating and Interpreting Forward Interest Rates: Sweden 1992 - 1994," NBER Working Papers 4871, National Bureau of Economic Research, Inc.
    9. Kaya, Huseyin, 2013. "Forecasting the yield curve and the role of macroeconomic information in Turkey," Economic Modelling, Elsevier, vol. 33(C), pages 1-7.
    10. Svensson, Lars E O, 1994. "Estimating and Interpreting Forward Interest Rates: Sweden 1992-4," CEPR Discussion Papers 1051, C.E.P.R. Discussion Papers.
    11. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    12. Michael D. Bauer & Thomas M. Mertens, 2018. "Economic Forecasts with the Yield Curve," FRBSF Economic Letter, Federal Reserve Bank of San Francisco.
    13. Nelson, Charles R & Siegel, Andrew F, 1987. "Parsimonious Modeling of Yield Curves," The Journal of Business, University of Chicago Press, vol. 60(4), pages 473-489, October.
    14. Fama, Eugene F & Bliss, Robert R, 1987. "The Information in Long-Maturity Forward Rates," American Economic Review, American Economic Association, vol. 77(4), pages 680-692, September.
    15. Marco Shinobu Matsumura & Ajax Reynaldo Bello Moreira & José Valentim Machado Vicente, 2010. "Forecasting the Yield Curve with Linear Factor Models," Working Papers Series 223, Central Bank of Brazil, Research Department.
    16. Diebold, Francis X. & Rudebusch, Glenn D. & Borag[caron]an Aruoba, S., 2006. "The macroeconomy and the yield curve: a dynamic latent factor approach," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 309-338.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Karam KIM & Doojin RYU, 2020. "Predictive ability of investor sentiment for the stock market," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 33-46, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gaus, Eric & Sinha, Arunima, 2018. "What does the yield curve imply about investor expectations?," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 248-265.
    2. Moreno, Manuel & Novales, Alfonso & Platania, Federico, 2018. "A term structure model under cyclical fluctuations in interest rates," Economic Modelling, Elsevier, vol. 72(C), pages 140-150.
    3. Wei-Choun Yu & Donald M. Salyards, 2009. "Parsimonious modeling and forecasting of corporate yield curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(1), pages 73-88.
    4. Christensen, Bent Jesper & Kjær, Mads Markvart & Veliyev, Bezirgen, 2023. "The incremental information in the yield curve about future interest rate risk," Journal of Banking & Finance, Elsevier, vol. 155(C).
    5. Audzeyeva, Alena & Fuertes, Ana-Maria, 2018. "On the predictability of emerging market sovereign credit spreads," Journal of International Money and Finance, Elsevier, vol. 88(C), pages 140-157.
    6. Caio Almeida & Kym Ardison & Daniela Kubudi & Axel Simonsen & José Vicente, 2018. "Forecasting Bond Yields with Segmented Term Structure Models," Journal of Financial Econometrics, Oxford University Press, vol. 16(1), pages 1-33.
    7. Guidolin, Massimo & Thornton, Daniel L., 2018. "Predictions of short-term rates and the expectations hypothesis," International Journal of Forecasting, Elsevier, vol. 34(4), pages 636-664.
    8. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2010. "Forecasting Government Bond Yields with Large Bayesian VARs," Working Papers 662, Queen Mary University of London, School of Economics and Finance.
    9. 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.
    10. Lelo de Larrea Alejandra, 2020. "Forecast Comparison of the Term Structure of Interest Rates of Mexico for Different Specifications of the Affine Model," Working Papers 2020-01, Banco de México.
    11. Elizondo Rocío, 2013. "Forecasting the Term Structure of Interest Rates in Mexico Using an Affine Model," Working Papers 2013-03, Banco de México.
    12. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Bond portfolio optimization using dynamic factor models," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 128-158.
    13. Ranik Raaen Wahlstrøm & Florentina Paraschiv & Michael Schürle, 2022. "A Comparative Analysis of Parsimonious Yield Curve Models with Focus on the Nelson-Siegel, Svensson and Bliss Versions," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 967-1004, March.
    14. Matsumura, Marco & Moreira, Ajax & Vicente, José, 2011. "Forecasting the yield curve with linear factor models," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 237-243.
    15. Gary S. Anderson & Alena Audzeyeva, 2019. "A Coherent Framework for Predicting Emerging Market Credit Spreads with Support Vector Regression," Finance and Economics Discussion Series 2019-074, Board of Governors of the Federal Reserve System (U.S.).
    16. Rui Liu, 2019. "Forecasting Bond Risk Premia with Unspanned Macroeconomic Information," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 9(01), pages 1-62, March.
    17. Carlo Altavilla & Raffaella Giacomini & Riccardo Costantini, 2014. "Bond Returns and Market Expectations," Journal of Financial Econometrics, Oxford University Press, vol. 12(4), pages 708-729.
    18. Daniel Borup & Jonas N. Eriksen & Mads M. Kjær & Martin Thyrsgaard, 2024. "Predicting Bond Return Predictability," Management Science, INFORMS, vol. 70(2), pages 931-951, February.
    19. Antonio Gargano & Davide Pettenuzzo & Allan Timmermann, 2019. "Bond Return Predictability: Economic Value and Links to the Macroeconomy," Management Science, INFORMS, vol. 65(2), pages 508-540, February.
    20. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2012. "Forecasting government bond yields with large Bayesian vector autoregressions," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2026-2047.

    More about this item

    Keywords

    Nelson-Siegel curve; term structure; dynamic factors; out-of-sample forecasting; random walk benchmark; long-term forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects

    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:rjr:romjef:v::y:2019:i:1:p:101-113. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Corina Saman (email available below). General contact details of provider: https://edirc.repec.org/data/ipacaro.html .

    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.