Python Package: Fitting and Forecasting the yield curve

some interest rate models such as Vasicek and dynamic Nelson-Siegel model

Dynamic Nelson Siegel Model

The R package offers a wide range of functions for term structure estimation based on static and dynamic coupon bond and yield data sets. The implementation focuses on the cubic splines approach of McCulloch (1971, 1975) and the Nelson and Siegel (1987) method with extensions by Svensson (1994), Diebold and Li (2006) and De Pooter (2007). We propose a weighted constrained optimization procedure with analytical gradients and a globally optimal start parameter search algorithm. Extensive summary statistics and plots are provided to compare the results of the different estimation methods. Several demos are available using data from European government bonds and yields.

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Dynamic Nelson-Siegel in two steps

Dynamic Nelson-Siegel and Svensson in two steps

Joint modeling of the nominal and real yield using Markov-Switching Dynamic Nelson-Siegel models

Dynamic Nelson-Siegel in one step

Dynamic Nelson-Siegel and Svensson in one step

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Arbitrage-free Dynamic Generalized Nelson-Siegel model of interest rates following Christensen, Diebold and Rudebusch; and its estimation using the Kalman filter / maximum likelihood.

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Tutorial about Dynamic-Nelson-Siegel-Svensson-Kalman-Filter Package

A toolkit-class for fixed income basics, like Nelson-Siegel and Svensson, incl. dynamic with Extended Kalman Filter, VAR forecasting of YC, scenario generation, CVaR optimizer, bond pricing and more

Dynamic Nelson-Siegel model with Kalman filter and Diebold-Mariano test.

Neste repositório estão os códigos para a geração dos parâmetros e variáveis a posteriori do modelo dinâmico de Nelson-Siegel.

Implemented the Dynamic Nelson and Siegel Model to forecast yield curves on US treasury bonds. Incorporated Markov chain-based regime switching for forecasting. Developed a robust backtester to compare actual and forecasted performance. Optimized a fixed income portfolio.

A repository to mess around in for my research-focused implementation of the Dynamic Nelson–Siegel (DNS) model using a state-space framework and Kalman filtering to estimate and forecast the yield curve.

Interest rate risk calculation with dynamic nelson siegel model

Python implementation of the Dynamic Nelson-Siegel term structure model

Using dynamic Nelson-Siegel model to fitting and forecasting the yield curve.

Replication of global yield curve dynamics and interactions: a dynamic nelson-siegel approach

R code/projects

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.

We develop an adaptive bond ladder strategy using optimal stopping, yield curve bootstrapping, and the Dynamic Nelson Siegel model with Kalman Filter. By decomposing cash and bond positions, we track value changes and identify optimal reinvestment points using real U.S. Treasury data.

We forecast the U.S. Treasury yield curve using the Dynamic Nelson-Siegel model in a state-space framework. A Kalman Filter estimates latent factors, and a GARCH(1,1) extension captures time-varying volatility. The KF-GARCH model improves uncertainty quantification, especially for long-term yields.

This a repository containing all related write-ups, research and code related to my final-year dissertation which holds the preliminary title - 'How Effectively Do Principal Component and Dynamic Nelson-Siegel Models Forecast Risk-Free Yield Curves? A Cross-Market Study of the US and South American Economies.'

DynamicYield models the U.S. Treasury yield curve using the Dynamic Nelson-Siegel framework with Kalman filtering, extracts latent factors (β₀, β₁, β₂), forecasts the 2s10s spread, & builds macro trading signals. It demonstrates real-world quant research techniques including state-space model, time series forecast &signal driven strategy simulation