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Efficient Yield Curve Estimation and Forecasting in Brazil

Author

Listed:
  • Ricardo Azevedo Araujo

    (Universidade de Brasilia (UnB))

  • Guilherme V. Moura

    (Vrije Universiteit Amsterdam, Netherlands)

  • Marcelo S. Portugal

    (Federal University of Rio Grande do Sul and CNPq, Brazil Abstract: Modeling the term structure of interest rate is very important to macroeconomists and financial market practitioners in general. In this paper, we used the Diebold-Li interpretation to the Nelson Siegel model in order to fit and forecast the Brazilian yield curve. The data consisted of daily observations of the most liquid future ID yields traded in the BM&F from January 2006 to February 2009. Differently from the literature on the Brazilian yield curve, where the Diebold-Li model is estimated through the two-step method, the model herein is put in the state-space form, and the parameters are simultaneously and efficiently estimated using the Kalman filter. The results obtained for the fit and for the forecast showed that the Kalman filter is the most suitable method for the estimation of the model, generating better forecast for all maturities when we consider the forecasting horizons of one and three months.)

Abstract

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Suggested Citation

  • Ricardo Azevedo Araujo & Guilherme V. Moura & Marcelo S. Portugal, 2010. "Efficient Yield Curve Estimation and Forecasting in Brazil," Economia, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics], vol. 11(1), pages 27-51.
    • Handle: RePEc:anp:econom:v:11:y:2010:i:1:27_51
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    References listed on IDEAS

    as
    1. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    2. Peter Hordahl & Oreste Tristani & David Vestin, 2003. "A joint econometric model of macroeconomic and term structure," Proceedings, Federal Reserve Bank of San Francisco, issue mar.
    3. Hordahl, Peter & Tristani, Oreste & Vestin, David, 2006. "A joint econometric model of macroeconomic and term-structure dynamics," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 405-444.
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    Cited by:

    1. Oleksandr Castello & Marina Resta, 2023. "A Machine-Learning-Based Approach for Natural Gas Futures Curve Modeling," Energies, MDPI, vol. 16(12), pages 1-22, June.
    2. Piero C. Kauffmann & Hellinton H. Takada & Ana T. Terada & Julio M. Stern, 2022. "Learning Forecast-Efficient Yield Curve Factor Decompositions with Neural Networks," Econometrics, MDPI, vol. 10(2), pages 1-15, March.
    3. 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.
    4. Caldeira, João F. & Laurini, Márcio P. & Portugal, Marcelo S., 2010. "Bayesian Inference Applied to Dynamic Nelson-Siegel Model with Stochastic Volatility," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 30(1), October.

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    Keywords

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • 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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