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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
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    References listed on IDEAS

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    1. John H. Cochrane & Monika Piazzesi, 2005. "Bond Risk Premia," American Economic Review, American Economic Association, vol. 95(1), pages 138-160, March.
    2. 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.
    3. Kaya, Huseyin, 2013. "Forecasting the yield curve and the role of macroeconomic information in Turkey," Economic Modelling, Elsevier, vol. 33(C), pages 1-7.
    4. Svensson, Lars E O, 1994. "Estimating and Interpreting Forward Interest Rates: Sweden 1992-4," CEPR Discussion Papers 1051, C.E.P.R. Discussion Papers.
    5. 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.
    6. Svensson, L.E.O., 1994. "Estimating and Interpreting Foreward Interest Rates: Sweden 1992-1994," Papers 579, Stockholm - International Economic Studies.
    7. Michael D. Bauer & Thomas M. Mertens, 2018. "Economic Forecasts with the Yield Curve," FRBSF Economic Letter, Federal Reserve Bank of San Francisco.
    8. 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.
    9. 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.
    10. 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.
    11. Chen, Ying & Niu, Linlin, 2014. "Adaptive dynamic Nelson–Siegel term structure model with applications," Journal of Econometrics, Elsevier, vol. 180(1), pages 98-115.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
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    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.

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    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

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