Skip to content
#

yield-curve-forecasting

Star

Here is 1 public repository matching this topic...

chirindaopensource / forecasting_the_US_treasury_yield_curve

Star 0

End-to-End Python implementation of Liu & Cheng's (2026) methodology for U.S. Treasury yield curve forecasting. Combines Factor-Augmented Dynamic Nelson-Siegel models, High-Dimensional Random Forests, and Distributionally Robust Optimization (DRO) for risk-aware ensemble forecasting under ambiguity.

python machine-learning random-forest reproducible-research econometrics scipy ensemble-learning quantitative-finance principal-component-analysis fixed-income risk-management time-series-analysis financial-modeling nelson-siegel-model shap-values forecast-combination treasury-bonds distributionally-robust-optimization structural-breaks yield-curve-forecasting
  • Updated Jan 11, 2026
  • Jupyter Notebook

Improve this page

Add a description, image, and links to the yield-curve-forecasting topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the yield-curve-forecasting topic, visit your repo's landing page and select "manage topics."

Learn more