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

TL;DR

This paper introduces a deep learning framework for arbitrage-free yield curve and bond price forecasting that incorporates HJM constraints, improving short-term prediction accuracy and market consistency.

Contribution

It develops a novel neural network architecture embedding no-arbitrage constraints using Kalman and particle filters combined with RNNs, with explicit arbitrage regularization during training.

Findings

Enhanced short-term forecast accuracy at 5-day horizon.

Improved market consistency as measured by bid-ask hit rates.

Reduced dollar-denominated prediction errors.

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.