Learning Forecast-Efficient Yield Curve Factor Decompositions with Neural Networks
Piero C. Kauffmann,
Hellinton H. Takada,
Ana T. Terada and
Julio M. Stern
Additional contact information
Piero C. Kauffmann: Institute of Mathematics and Statistics, University of Sao Paulo, Sao Paulo 05508-090, Brazil
Hellinton H. Takada: Santander Asset Management, Sao Paulo 04543-011, Brazil
Ana T. Terada: Institute of Mathematics and Statistics, University of Sao Paulo, Sao Paulo 05508-090, Brazil
Julio M. Stern: Institute of Mathematics and Statistics, University of Sao Paulo, Sao Paulo 05508-090, Brazil
Econometrics, 2022, vol. 10, issue 2, 1-15
Abstract:
Most factor-based forecasting models for the term structure of interest rates depend on a fixed number of factor loading functions that have to be specified in advance. In this study, we relax this assumption by building a yield curve forecasting model that learns new factor decompositions directly from data for an arbitrary number of factors, combining a Gaussian linear state-space model with a neural network that generates smooth yield curve factor loadings. In order to control the model complexity, we define prior distributions with a shrinkage effect over the model parameters, and we present how to obtain computationally efficient maximum a posteriori numerical estimates using the Kalman filter and automatic differentiation. An evaluation of the model’s performance on 14 years of historical data of the Brazilian yield curve shows that the proposed technique was able to obtain better overall out-of-sample forecasts than traditional approaches, such as the dynamic Nelson and Siegel model and its extensions.
Keywords: yield curve forecasting; neural networks; machine learning; bayesian modeling; yield curve decomposition; dynamic factor models; Kalman filter (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:10:y:2022:i:2:p:15-:d:780065
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