Learning forecast-efficient yield curve factor decompositions with neural networks
Piero C. Kauffmann, Hellinton H. Takada, Ana T. Terada and Julio M. Stern
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.
Year of publication: |
2022
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Authors: | Kauffmann, Piero C. ; Takada, Hellinton H. ; Terada, Ana T. ; Stern, Julio |
Published in: |
Econometrics : open access journal. - Basel : MDPI, ISSN 2225-1146, ZDB-ID 2717594-7. - Vol. 10.2022, 2, Art.-No. 15, p. 1-15
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Subject: | yield curve forecasting | neural networks | machine learning | bayesian modeling | yield curve decomposition | dynamic factor models | Kalman filter | Neuronale Netze | Neural networks | Zinsstruktur | Yield curve | Prognoseverfahren | Forecasting model | Theorie | Theory | Zustandsraummodell | State space model | Faktorenanalyse | Factor analysis | Bayes-Statistik | Bayesian inference | Dekompositionsverfahren | Decomposition method | Schätzung | Estimation |
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