Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/339244 
Year of Publication: 
2026
Series/Report no.: 
Tinbergen Institute Discussion Paper No. TI 2026-007/III
Publisher: 
Tinbergen Institute, Amsterdam and Rotterdam
Abstract: 
We propose a factor model with time-varying loadings for term structure modeling and fore casting. While maintaining the interpretation of the factors as level, slope, and curvature through explicit identification restrictions, we allow the loadings to take flexible shapes by specifying them as neural networks that evolve over time using a "self-driving" updating scheme based on past forecast errors, with gradient scaling to improve robustness. Using an empirically calibrated simulation study and an application to U.S. Treasury yields across 24 maturities, we show that flexible and dynamic factor loadings improve forecasting performance relative to standard benchmarks, including Nelson-Siegel models and the random walk. The gains are strongest at medium maturities and shorter forecast horizons, highlighting the importance of capturing curvature dynamics. In-sample results further illustrate how time-varying loadings provide insight into changes in yield curve shape beyond traditional parametric specifications.
Subjects: 
time-varying neural networks
observation-driven dynamics
yield curve
JEL: 
C38
C45
E43
Document Type: 
Working Paper

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