Abstract:This paper introduces a physics-informed generative framework that resolves the fundamental conflict between the statistical flexibility of deep learning and the rigorous theoretical constraints of fixed-income modeling. We demonstrate that standard generative models and unconstrained statistical extrapolations suffer from "manifold collapse" and severe arbitrage violations when forecasting term structures across diverse macroeconomic regimes. To overcome this, we propose a two-stage architecture. First, a Student-t Conditional Variational Autoencoder with Dynamic Level Injection (CVAEsT+LS) extracts a robust, heavy-tailed term structure manifold, effectively decoupling macroeconomic shape dynamics from absolute base rates. Second, the latent dynamic evolution is governed by a continuous-time Neural Stochastic Differential Equation (SDE) strictly penalized by a No-Arbitrage Partial Differential Equation (PDE). Empirical results across multiple sovereign currencies (USD, GBP, JPY) confirm that our synergistic approach drastically reduces out-of-sample forecasting errors -- achieving an exceptional 6.58 bps Mean Tenor RMSE -- and successfully overcomes the massive parallel drift and zero-lower-bound violations exhibited by the classical HJM model in extreme environments. Furthermore, through phase space vector field analysis, we demonstrate the model's superior capability in unsupervised macroeconomic regime detection and high-quality continuous-time scenario generation. Ultimately, this research provides a highly scalable, mathematically sound evolutionary engine for term structure modeling.
| Comments: | This is the full script of our paper, which is awaiting submission to financial journals/conferences |
| Subjects: | Mathematical Finance (q-fin.MF); Machine Learning (cs.LG); Machine Learning (stat.ML) |
| Cite as: | arXiv:2605.12764 [q-fin.MF] |
| (or arXiv:2605.12764v1 [q-fin.MF] for this version) | |
| https://doi.org/10.48550/arXiv.2605.12764 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Fusheng Luo [view email]
[v1]
Tue, 12 May 2026 21:21:25 UTC (4,271 KB)