Ultimate Forward Rate Prediction and its Application to Bond Yield Forecasting: A Machine Learning Perspective

ArXiv ID: 2601.00011 “View on arXiv”

Authors: Jiawei Du, Yi Hong

Abstract

This study focuses on forecasting the ultimate forward rate (UFR) and developing a UFRbased bond yield prediction model using data from Chinese treasury bonds and macroeconomic variables spanning from December 2009 to December 2024. The de Kort-Vellekooptype methodology is applied to estimate the UFR, incorporating the optimal turning parameter determination technique proposed in this study, which helps mitigate anomalous fluctuations. In addition, both linear and nonlinear machine learning techniques are employed to forecast the UFR and ultra-long-term bond yields. The results indicate that nonlinear machine learning models outperform their linear counterparts in forecasting accuracy. Incorporating macroeconomic variables, particularly price index-related variables, significantly improves the accuracy of predictions. Finally, a novel UFR-based bond yield forecasting model is developed, demonstrating superior performance across different bond maturities.

Keywords: Ultimate Forward Rate (UFR), Machine Learning Forecasting, Nonlinear Models, Macroeconomic Variables, Bond Yield Prediction, Fixed Income (Chinese Treasury Bonds)

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical methods like the Smith-Wilson framework and de Kort-Vellekoop-type estimation with parameter optimization, requiring strong mathematical understanding. It demonstrates high empirical rigor through extensive backtesting on 15 years of Chinese treasury and macroeconomic data, utilizing both linear and nonlinear machine learning models (including neural networks), SHAP interpretability analysis, and clear out-of-sample performance metrics.

  flowchart TD
    A["Research Goal: UFR & Bond Yield<br>Forecasting using ML"] --> B["Data Collection<br>Treasury Bonds & Macro Variables<br>Dec 2009 - Dec 2024"]
    
    B --> C["Methodology Application<br>1. de Kort-Vellekoom UFR Estimation<br>2. Optimal Turning Parameter<br>3. Linear & Nonlinear ML Models"]
    
    C --> D["Computational Analysis<br>Model Training &<br>UFR/Bond Yield Forecasting"]
    
    D --> E["Key Findings & Outcomes<br>• Nonlinear ML > Linear Models<br>• Macro Variables (Price Index) boost accuracy<br>• Novel UFR-based bond yield model developed"]