Robust Yield Curve Estimation for Mortgage Bonds Using Neural Networks

TL;DR

This paper introduces a neural network-based framework for robust and stable yield curve estimation in small mortgage bond markets, addressing data noise and sparsity issues more effectively than traditional models.

Contribution

It proposes a novel neural network approach with a custom loss function for yield curve estimation, improving robustness and stability over existing methods.

Findings

Outperforms Nelson-Siegel-Svensson and Kernel-Ridge in stability and robustness

Enables integration of domain-specific constraints like risk-free benchmarks

Provides more accurate yield curves in noisy, sparse data environments

Abstract

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.