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A Regime-Switching Nelson--Siegel Term Structure Model and Interest Rate Forecasts

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  • Ju Xiang
  • Xiaoneng Zhu

Abstract

This article presents a dynamic Nelson--Siegel term structure model subject to regime shifts. To estimate the model, we introduce the reversible jump Markov chain Monte Carlo method, which allows jumps between the one-, two-, and three-regime models. The empirical results support the two-regime Nelson--Siegel term structure model. The empirical results also suggest that the regime-switching Nelson--Siegel term structure model forecasts better out-of-sample than the single-regime Nelson--Siegel model and other competing models. In addition, our economic analysis is favorable to the better forecasting performance of the regime-switching Nelson--Siegel model. Using the Diebold-Li bond yields, we find that the better forecasting performance is robust. Finally, two regimes are found to be related to business cycle conditions and monetary policy. Copyright The Author, 2013. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com, Oxford University Press.

Suggested Citation

  • Ju Xiang & Xiaoneng Zhu, 2013. "A Regime-Switching Nelson--Siegel Term Structure Model and Interest Rate Forecasts," Journal of Financial Econometrics, Oxford University Press, vol. 11(3), pages 522-555, June.
    • Handle: RePEc:oup:jfinec:v:11:y:2013:i:3:p:522-555
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbs021
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    Cited by:

    1. Horváth, Lajos & Liu, Zhenya & Rice, Gregory & Wang, Shixuan, 2020. "A functional time series analysis of forward curves derived from commodity futures," International Journal of Forecasting, Elsevier, vol. 36(2), pages 646-665.
    2. Lajos Horváth & Zhenya Liu & Curtis Miller & Weiqing Tang, 2024. "Breaks in term structures: Evidence from the oil futures markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2317-2341, April.
    3. 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.
    4. Fausto Vieira & Fernando Chague, Marcelo Fernandes, 2016. "A dynamic Nelson-Siegel model with forward-looking indicators for the yield curve in the US," Working Papers, Department of Economics 2016_31, University of São Paulo (FEA-USP).
    5. Shang, Fei, 2022. "The effect of uncertainty on the sensitivity of the yield curve to monetary policy surprises," Journal of Economic Dynamics and Control, Elsevier, vol. 137(C).
    6. Levant, Jared & Ma, Jun, 2017. "A dynamic Nelson-Siegel yield curve model with Markov switching," Economic Modelling, Elsevier, vol. 67(C), pages 73-87.
    7. Zhu, Xiaoneng & Rahman, Shahidur, 2015. "A regime-switching Nelson–Siegel term structure model of the macroeconomy," Journal of Macroeconomics, Elsevier, vol. 44(C), pages 1-17.
    8. Gaus, Eric & Sinha, Arunima, 2018. "What does the yield curve imply about investor expectations?," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 248-265.
    9. Constantino Hevia & Martin Gonzalez‐Rozada & Martin Sola & Fabio Spagnolo, 2015. "Estimating and Forecasting the Yield Curve Using A Markov Switching Dynamic Nelson and Siegel Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(6), pages 987-1009, September.
    10. Guidolin, Massimo & Pedio, Manuela, 2019. "Forecasting and trading monetary policy effects on the riskless yield curve with regime switching Nelson–Siegel models," Journal of Economic Dynamics and Control, Elsevier, vol. 107(C), pages 1-1.
    11. 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.
    12. Choi, Ahjin & Kang, Kyu Ho, 2023. "Modeling the time-varying dynamic term structure of interest rates," Journal of Banking & Finance, Elsevier, vol. 153(C).
    13. Siyu Bie & Francis X. Diebold & Jingyu He & Junye Li, 2024. "Machine Learning and the Yield Curve: Tree-Based Macroeconomic Regime Switching," Papers 2408.12863, arXiv.org.
    14. 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.
    15. Gordon H. Dash & Nina Kajiji & Domenic Vonella, 2018. "The role of supervised learning in the decision process to fair trade US municipal debt," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 6(1), pages 139-168, June.
    16. Giuseppe Arbia & Michele Di Marcantonio, 2015. "Forecasting Interest Rates Using Geostatistical Techniques," Econometrics, MDPI, vol. 3(4), pages 1-28, November.

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