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Forecasting interest rates

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  • Gregory R. Duffee

Abstract

This chapter discusses what the asset-pricing literature concludes about the forecastability of interest rates. It outlines forecasting methodologies implied by this literature, including dynamic, no-arbitrage term structure models and their macro-finance extensions. It also reviews the empirical evidence concerning the predictability of future yields on Treasury bonds and future excess returns to holding these bonds. In particular, it critically evaluates theory and evidence that variables other than current bond yields are useful in forecasting.

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  • Gregory R. Duffee, 2012. "Forecasting interest rates," Economics Working Paper Archive 599, The Johns Hopkins University,Department of Economics.
  • Handle: RePEc:jhu:papers:599
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    5. Rasmus Fatum & Naoko Hara & Yohei Yamamoto, 2019. "Negative Interest Rate Policy and the Influence of Macroeconomic News on Yields," IMES Discussion Paper Series 19-E-02, Institute for Monetary and Economic Studies, Bank of Japan.
    6. Richard K. Crump & Nikolay Gospodinov, 2022. "On the Factor Structure of Bond Returns," Econometrica, Econometric Society, vol. 90(1), pages 295-314, January.
    7. Zura Kakushadze & Willie Yu, 2020. "Machine Learning Treasury Yields," Bulletin of Applied Economics, Risk Market Journals, vol. 7(1), pages 1-65.
    8. Mikhail Chernov & Drew D. Creal & Peter Hördahl, 2020. "Sovereign Credit and Exchange Rate Risks: Evidence from Asia-Pacific Local Currency Bonds," NBER Working Papers 27500, National Bureau of Economic Research, Inc.
    9. Garriga, Carlos & Kydland, Finn E. & Šustek, Roman, 2021. "MoNK: Mortgages in a New-Keynesian model," Journal of Economic Dynamics and Control, Elsevier, vol. 123(C).
    10. Peter Feldhütter, 2016. "Can Affine Models Match the Moments in Bond Yields?," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 6(02), pages 1-56, June.
    11. Pincheira, Pablo M. & West, Kenneth D., 2016. "A comparison of some out-of-sample tests of predictability in iterated multi-step-ahead forecasts," Research in Economics, Elsevier, vol. 70(2), pages 304-319.
    12. Shin, Minchul & Zhong, Molin, 2017. "Does realized volatility help bond yield density prediction?," International Journal of Forecasting, Elsevier, vol. 33(2), pages 373-389.
    13. Kaminska, Iryna & Mumtaz, Haroon & Šustek, Roman, 2021. "Monetary policy surprises and their transmission through term premia and expected interest rates," Journal of Monetary Economics, Elsevier, vol. 124(C), pages 48-65.
    14. Zura Kakushadze & Willie Yu, 2020. "Machine Learning Treasury Yields," Papers 2003.05095, arXiv.org.
    15. 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.
    16. Giuseppe Arbia & Michele Di Marcantonio, 2015. "Forecasting Interest Rates Using Geostatistical Techniques," Econometrics, MDPI, vol. 3(4), pages 1-28, November.
    17. Bretscher, Lorenzo & Hsu, Alex & Tamoni, Andrea, 2020. "Fiscal policy driven bond risk premia," Journal of Financial Economics, Elsevier, vol. 138(1), pages 53-73.
    18. Samuel G Hanson & David O Lucca & Jonathan H Wright, 2021. "Rate-Amplifying Demand and the Excess Sensitivity of Long-Term Rates," The Quarterly Journal of Economics, Oxford University Press, vol. 136(3), pages 1719-1781.
    19. Stolyarov, Dmitriy & Tesar, Linda L., 2021. "Interest rate trends in a global context," Economic Modelling, Elsevier, vol. 101(C).
    20. Doshi, Hitesh & Jacobs, Kris & Liu, Rui, 2018. "Macroeconomic determinants of the term structure: Long-run and short-run dynamics," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 99-122.

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