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Arbitrage-Free Bond and Yield Curve Forecasting with Neural Filters under HJM Constraints

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  • Xiang Gao
  • Cody Hyndman

Abstract

We develop an arbitrage-free deep learning framework for yield curve and bond price forecasting based on the Heath-Jarrow-Morton (HJM) term-structure model and a dynamic Nelson-Siegel parameterization of forward rates. Our approach embeds a no-arbitrage drift restriction into a neural state-space architecture by combining Kalman, extended Kalman, and particle filters with recurrent neural networks (LSTM/CLSTM), and introduces an explicit arbitrage error regularization (AER) term during training. The model is applied to U.S. Treasury and corporate bond data, and its performance is evaluated for both yield-space and price-space predictions at 1-day and 5-day horizons. Empirically, arbitrage regularization leads to its strongest improvements at short maturities, particularly in 5-day-ahead forecasts, increasing market-consistency as measured by bid-ask hit rates and reducing dollar-denominated prediction errors.

Suggested Citation

  • Xiang Gao & Cody Hyndman, 2025. "Arbitrage-Free Bond and Yield Curve Forecasting with Neural Filters under HJM Constraints," Papers 2511.17892, arXiv.org.
    • Handle: RePEc:arx:papers:2511.17892
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