Self-driving neural networks for term structure modeling

Sicco Kooiker, Janneke van Brummelen, Julia Schaumburg and Marcin Zamojski
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Sicco Kooiker: Vrije Universiteit Amsterdam
Janneke van Brummelen: Vrije Universiteit Amsterdam
Julia Schaumburg: Vrije Universiteit Amsterdam
Marcin Zamojski: Vrije Universiteit Amsterdam

No 26-007/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: We propose a factor model with time-varying loadings for term structure modeling and forecasting. 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.

Keywords: time-varying neural networks; observation-driven dynamics; yield curve (search for similar items in EconPapers)
JEL-codes: C38 C45 E43 (search for similar items in EconPapers)
Date: 2026-02-26
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