Arbitrage-Free Bond and Yield Curve Forecasting with Neural Filters under HJM Constraints
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Abstract (arXiv)
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.
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References · 8
- 2020Model-Based Deep Learningcited by 141
- 2021KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamicscited by 132
- 2021Arbitrage-free neural-SDE market modelscited by 19
- 2023Bayesian KalmanNet: Quantifying Uncertainty in Deep Learning Augmented Kalman Filtercited by 10
- 2023Transformers Can Solve Non-Linear and Non-Markovian Filtering Problems in Continuous Time For Conditionally Gaussian Signalscited by 4
- 2017Machine Learning for Better Models for Predicting Bond Pricescited by 1
- 2023A stochastic control perspective on term structure models with roll-over riskcited by 1
- 2024Multiple Yield Curve Modeling and Forecasting using Deep Learningcited by 1
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