Robust Yield Curve Estimation for Mortgage Bonds Using Neural Networks
This work addresses the challenge of accurate yield curve estimation for practitioners in fixed-income markets, particularly for mortgage bonds, but it is incremental as it adapts neural networks to a specific domain with a new loss function.
The paper tackled the problem of robust yield curve estimation in small mortgage bond markets, where traditional methods like Nelson-Siegel-Svensson and Kernel-Ridge struggle with overfitting and instability due to sparse and noisy data. The result was a neural network-based framework that delivered more robust and stable yield curve estimates, as demonstrated empirically on Swedish mortgage bonds.
Robust yield curve estimation is crucial in fixed-income markets for accurate instrument pricing, effective risk management, and informed trading strategies. Traditional approaches, including the bootstrapping method and parametric Nelson-Siegel models, often struggle with overfitting or instability issues, especially when underlying bonds are sparse, bond prices are volatile, or contain hard-to-remove noise. In this paper, we propose a neural networkbased framework for robust yield curve estimation tailored to small mortgage bond markets. Our model estimates the yield curve independently for each day and introduces a new loss function to enforce smoothness and stability, addressing challenges associated with limited and noisy data. Empirical results on Swedish mortgage bonds demonstrate that our approach delivers more robust and stable yield curve estimates compared to existing methods such as Nelson-Siegel-Svensson (NSS) and Kernel-Ridge (KR). Furthermore, the framework allows for the integration of domain-specific constraints, such as alignment with risk-free benchmarks, enabling practitioners to balance the trade-off between smoothness and accuracy according to their needs.