ArXiv

Data-Driven Duration Management -- Term Structure Forecasting Using Machine Learning

2026-06-25 · 09:58 UTC ·Tobias Lausser, Joao Eduardo Vuolo, Rudi Zagst ·1 min read
Authors
Tobias Lausser, Joao Eduardo Vuolo, Rudi Zagst
Categories
q-fin.PM, q-fin.CP, stat.ML
arXiv
https://arxiv.org/abs/2606.26815v1
PDF
https://arxiv.org/pdf/2606.26815v1

Brief

The paper evaluates forecasting of U.S. and European zero‑coupon yield curves by comparing classical approaches (Dynamic Nelson‑Siegel, PCA) with multiple neural‑network architectures, incorporating macroeconomic inputs and Autoencoders. Using statistical metrics (RMSE, MAE, directional accuracy) plus a bond‑trading performance test, the authors find NNs improve both forecast accuracy and portfolio returns; optimal architectures differ across the two markets.

Why it matters

Neural networks consistently beat classical term‑structure methods (Dynamic Nelson‑Siegel, PCA) on both U.S. Treasury and ECB zero‑coupon bond forecasts and in downstream portfolio performance; evaluation used RMSE, MAE, directional accuracy plus an economic bond‑trading metric (Lausser et al., 2026‑06‑25).

Key details

  • Best models differ by market: for the U.S. a direct‑forecasting NN that uses DNS factors for zero‑rate dimensionality reduction and an Autoencoder to extract macroeconomic features performed best; for Europe a factor‑based NN using PCA‑derived zero‑rate factors without macro integration was optimal.
Source evidence

Abstract

This paper compares different methods for forecasting the term structure of U.S. and European zero-coupon government bonds using both traditional econometric and Machine Learning (ML) approaches. We compare classical models (e.g., Dynamic Nelson-Siegel (DNS) and Principal Component Analysis (PCA)) with different Neural Network (NN) architectures, including those inspired by the classical models, on the U.S. Treasury market and bonds issued by the European Central Bank (ECB). To enhance predictive performance, macroeconomic variables are incorporated. The findings for both markets are separately analyzed and compared. To this end, we propose a robust model evaluation framework combining statistical accuracy metrics - such as RMSE, MAE, and directional accuracy - with the economic relevance of a quantitative bond trading strategy. Results show that NNs consistently outperform traditional models in both forecasting accuracy and portfolio performance. For the U.S., the most effective approach is a direct-forecasting NN that incorporates DNS factors to reduce the dimensionality of zero-rate data and an Autoencoder (AE) to extract macroeconomic features, while for Europe, the optimal model is a factor-based NN using PCA-derived zero-rate factors without the integration of macroeconomic variables. Overall, the paper demonstrates how combining traditional modeling approaches with modern ML techniques and evaluation can improve yield curve forecasts and support applications in fixed-income portfolio construction.