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Reconstructing the Yield Curve

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  • Yan Liu
  • Jing Cynthia Wu

Abstract

The constant-maturity zero-coupon Treasury yield curve is one of the most studied datasets. We reconstruct the yield curve using a non-parametric kernel-smoothing method with a novel adaptive bandwidth specifically designed to fit the Treasury yield curve. Our curve is globally smooth while still capturing important local variation. Economically, we show that applying our data leads to different conclusions from using the leading alternative data of Gürkaynak et al. (2007) (GSW) when we repeat two popular studies of Cochrane and Piazzesi (2005) and Giglio and Kelly (2018). Statistically, we show our dataset preserves information in the raw data and has much smaller pricing errors than GSW. Our new yield curve is maintained and updated online, complemented by bandwidths that summarize information content in the raw data.

Suggested Citation

  • Yan Liu & Jing Cynthia Wu, 2020. "Reconstructing the Yield Curve," NBER Working Papers 27266, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:27266
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    2. Rita Pimentel & Morten Risstad & Sjur Westgaard, 2022. "Predicting interest rate distributions using PCA & quantile regression," Digital Finance, Springer, vol. 4(4), pages 291-311, December.
    3. Nagel, Stefan & Xu, Zhengyang, 2023. "Dynamics of subjective risk premia," Journal of Financial Economics, Elsevier, vol. 150(2).
    4. Sihvonen, Markus, 2021. "Yield curve momentum," Bank of Finland Research Discussion Papers 15/2021, Bank of Finland.
    5. Jamie L. Cross & Aubrey Poon & Dan Zhu, 2023. "Uncertainty and the Term Structure of Interest Rates," Working Papers No 12/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    6. Gaygysyz Guljanov & Willi Mutschler & Mark Trede, 2022. "Pruned Skewed Kalman Filter and Smoother: With Application to the Yield Curve," CQE Working Papers 10122, Center for Quantitative Economics (CQE), University of Muenster.
    7. Han, Yang & Jiao, Anqi & Ma, Jun, 2021. "The predictive power of Nelson–Siegel factor loadings for the real economy," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 95-127.
    8. Sihvonen, Markus, 2021. "Yield curve momentum," Research Discussion Papers 15/2021, Bank of Finland.
    9. Francesco Bravo, 2022. "Misspecified semiparametric model selection with weakly dependent observations," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(4), pages 558-586, July.
    10. Boeckx, Jef & Iania, Leonardo & Wauters, Joris, 2023. "Macroeconomic drivers of Inflation Expectations and Inflation Risk Premia," LIDAM Discussion Papers LFIN 2023003, Université catholique de Louvain, Louvain Finance (LFIN).
    11. Renee van Eyden & Rangan Gupta & Jacobus Nel & Elie Bouri, 2021. "Rare Disaster Risks and Volatility of the Term-Structure of US Treasury Securities: The Role of El Nino and La Nina Events," Working Papers 202155, University of Pretoria, Department of Economics.
    12. Alfaro, Rodrigo & Piña, Marco, 2023. "Estimates of the US Shadow-Rate," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(1).
    13. Dennis Schroers, 2024. "Robust Functional Data Analysis for Stochastic Evolution Equations in Infinite Dimensions," Papers 2401.16286, arXiv.org.
    14. repec:zbw:bofrdp:2021_015 is not listed on IDEAS
    15. Matteo Iacopini & Aubrey Poon & Luca Rossini & Dan Zhu, 2024. "A Quantile Nelson-Siegel model," Papers 2401.09874, arXiv.org.
    16. Andrew J. Patton & Yasin Simsek, 2023. "Generalized Autoregressive Score Trees and Forests," Papers 2305.18991, arXiv.org.
    17. Rodrigo Alfaro & Marco Piña, 2021. "Estimates of the US Shadow-Rate," Working Papers Central Bank of Chile 923, Central Bank of Chile.

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    JEL classification:

    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects

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