用神经滤波器构建无套利利率曲线预测模型，提升短期预测准确性。
Arbitrage-Free Bond and Yield Curve Forecasting with Neural Filters under HJM Constraints
- 结合HJM模型与LSTM，嵌入无套利约束的动态滤波架构
- 5天预测中短债误差降低，买卖价差命中率显著提升
- 适合量化金融、债券交易员用于高精度利率风险建模
我们提出一种基于赫斯-贾罗-莫顿（HJM）期限结构模型和前向利率动态Nelson-Siegel参数化的无套利深度学习框架，用于收益率曲线与债券价格预测。通过将无套利漂移约束融入卡尔曼、扩展卡尔曼及粒子滤波器与循环神经网络（LSTM/CLSTM）的组合架构，并在训练中引入显式的套利误差正则化（AER）项。模型应用于美国国债与企业债数据，在1天与5天预测期内评估了收益率空间与价格空间的性能。实证表明，套利正则化在短期期限上效果最显著，尤其在5天前瞻预测中，提升了市场一致性（以买卖价差命中率衡量），并降低了美元计价的预测误差。
原文摘要 · Abstract (English)
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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