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Forecasting Bond Yields with Segmented Term Structure Models

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  • Caio Almeida
  • Axel Simonsen
  • José Vicente

Abstract

Recent empirical analysis of interest rate markets documents that bond demand and supply directly affect yield curve movements and bond risk premium. Motivated by those findings we propose a parametric interest rate model that allows for segmentation and local shocks in the term structure. We split the yield curve in segments presenting their own local movements that are globally interconnected by smoothing conditions. Two classes of segmented exponential models are derived and compared to successful term structure models based on a sequence of out-of-sample forecasting exercises. Adopting U.S. interest rates data available from 1985 to 2008, the segmented models present overall better forecasting performance suggesting that local shocks might indeed be important determinants of yield curve dynamics.

Suggested Citation

  • Caio Almeida & Axel Simonsen & José Vicente, 2012. "Forecasting Bond Yields with Segmented Term Structure Models," Working Papers Series 288, Central Bank of Brazil, Research Department.
  • Handle: RePEc:bcb:wpaper:288
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    2. Duffee, Gregory, 2013. "Forecasting Interest Rates," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 385-426, Elsevier.
    3. Papadimitriou, Theophilos & Gogas, Periklis & Tabak, Benjamin M., 2013. "Complex networks and banking systems supervision," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4429-4434.
    4. 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.
    5. Zura Kakushadze & Willie Yu, 2020. "Machine Learning Treasury Yields," Bulletin of Applied Economics, Risk Market Journals, vol. 7(1), pages 1-65.
    6. Feng, Pan & Qian, Junhui, 2018. "Forecasting the yield curve using a dynamic natural cubic spline model," Economics Letters, Elsevier, vol. 168(C), pages 73-76.
    7. Carlos Castro-Iragorri & Juan Felipe Peña & Cristhian Rodríguez, 2021. "A Segmented and Observable Yield Curve for Colombia," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 10(2), pages 179-200.
    8. Lozano-Espitia, Ignacio & Julio-Román, J. Manuel, 2020. "Debt limits and fiscal space for some Latin American economies," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    9. Zura Kakushadze & Willie Yu, 2020. "Machine Learning Treasury Yields," Papers 2003.05095, arXiv.org.

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    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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