Yield Curve Dynamics Using Variational Autoencoders Under No-arbitrage
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摘要
This paper introduces a physics-informed generative framework that resolves the conflict between statistical flexibility and rigorous fixed-income modeling. A two-stage architecture—Stage A CVAEsT + LS (Student-t Conditional VAE with Dynamic Level Injection) and Stage B a continuous-time Neural SDE penalized by a No-Arbitrage PDE—yields an arbitrage-free, heavy-tailed yield-curve manifold. Empirical results across USD, GBP, and JPY show markedly reduced out-of-sample errors (6.58 bps Mean Tenor RMSE), overcoming issues seen in classical HJM and unconstrained baselines, while enabling regime detection and robust scenario generation [page::1]
