Efficient Yield Curve Estimation and Forecasting in Brazil
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References listed on IDEAS
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Cited by:
- 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.
- 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.
- 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.
- 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.
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More about this item
Keywords
Term Structure of the Interest Rate; Yield Curve; State-Space Model; Kalman Filter.;All these keywords.
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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