Learning forecast-efficient yield curve factor decompositions with neural networks
Year of publication: |
2022
|
---|---|
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
|
Subject: | yield curve forecasting | neural networks | machine learning | bayesian modeling | yield curve decomposition | dynamic factor models | Kalman filter | Zinsstruktur | Yield curve | Neuronale Netze | Neural networks | Prognoseverfahren | Forecasting model | Theorie | Theory | Zustandsraummodell | State space model | Dekompositionsverfahren | Decomposition method | Faktorenanalyse | Factor analysis | Bayes-Statistik | Bayesian inference | Schätzung | Estimation | Künstliche Intelligenz | Artificial intelligence | Lernprozess | Learning process | Kapitaleinkommen | Capital income |
-
Deep learning, predictability, and optimal portfolio returns
Babiak, Mykola, (2020)
-
Forecasting the yield curve of government bonds : a dynamic factor approach
Ben Omrane, Walid, (2017)
-
Audrino, Francesco, (2016)
- More ...
-
Costa, Oswaldo Luiz do Valle, (2017)
-
Stern, Julio Michael, (2018)
- More ...