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Forecasting the Brazilian Yield Curve Using Forward-Looking Variables

Author

Listed:
  • Fausto Vieira

    (Fundação Getulio Vargas (FGV))

  • Fernando Chague

    (University of São Paulo)

  • Marcelo Fernandes

    (Queen Mary University of London)

Abstract

This paper proposes a forecasting model that combines a factor augmented VAR (FAVAR) methodology with the Nelson and Siegel (NS) parametrization of the yield curve to predict the Brazilian term structure of interest rates. Importantly, we extract the principal components for the FAVAR from a large data set containing forward-looking macroeconomic and financial variables. Our forecasting model significantly improves the predicting accuracy of extant models in the literature, particularly at short-term horizons. For instance, the mean absolute forecast errors are 15-40% lower than the random walk benchmark on predictions at the three month horizon. The out-of-sample analysis shows that including forward-looking indicators is the key to improve the predictive ability of the model.

Suggested Citation

  • Fausto Vieira & Fernando Chague & Marcelo Fernandes, 2016. "Forecasting the Brazilian Yield Curve Using Forward-Looking Variables," Working Papers 799, Queen Mary University of London, School of Economics and Finance.
    • Handle: RePEc:qmw:qmwecw:799
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    Cited by:

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    2. João Frois Caldeira & Rangan Gupta & Muhammad Tahir Suleman & Hudson S. Torrent, 2021. "Forecasting the Term Structure of Interest Rates of the BRICS: Evidence from a Nonparametric Functional Data Analysis," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(15), pages 4312-4329, December.
    3. Firdous Ahmad Shah & Lokenath Debnath, 2017. "Wavelet Neural Network Model for Yield Spread Forecasting," Mathematics, MDPI, vol. 5(4), pages 1-15, November.
    4. Sang-Heon Lee, 2025. "An Alternative Approach for Determining the Time-Varying Decay Parameter of the Nelson-Siegel Model," Computational Economics, Springer;Society for Computational Economics, vol. 65(5), pages 2965-2990, May.
    5. Albani, V.V.L. & Marcavillaca, R.T. & Moreira, P.S.E. & Avila, S.L. & Geremia, M. & Piovezan, R.P.B. & Sica, E.T. & Santos, E., 2025. "Short-term forecasting of forward prices in the Brazilian electricity market with a hybrid stochastic-neural network model," Energy Economics, Elsevier, vol. 148(C).
    6. 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).
    7. Chen, Jiazi & Hong, Zhiwu & Niu, Linlin, 2025. "Forecasting interest rates with shifting endpoints: The role of the functional demographic age distribution," International Journal of Forecasting, Elsevier, vol. 41(1), pages 153-174.
    8. Ronald Ravinesh Kumar & Peter Josef Stauvermann & Hang Thi Thu Vu, 2021. "The Relationship between Yield Curve and Economic Activity: An Analysis of G7 Countries," JRFM, MDPI, vol. 14(2), pages 1-23, February.
    9. Fernandes, Marcelo & Vieira, Fausto, 2019. "A dynamic Nelson–Siegel model with forward-looking macroeconomic factors for the yield curve in the US," Journal of Economic Dynamics and Control, Elsevier, vol. 106(C), pages 1-1.

    More about this item

    Keywords

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    JEL classification:

    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

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