{"ID":2838171,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.17892","arxiv_id":"2511.17892","title":"Arbitrage-Free Bond and Yield Curve Forecasting with Neural Filters under HJM Constraints","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.","short_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 combin...","url_abs":"https://arxiv.org/abs/2511.17892","url_pdf":"https://arxiv.org/pdf/2511.17892v1","authors":"[\"Xiang Gao\",\"Cody Hyndman\"]","published":"2025-11-22T02:47:27Z","proceeding":"q-fin.MF","tasks":"[\"q-fin.MF\",\"cs.LG\",\"q-fin.CP\",\"stat.ML\"]","methods":"[]","has_code":false}