Multiple Yield Curve Modeling and Forecasting using Deep Learning
This work addresses the need for more accurate yield curve forecasts in finance, leveraging globalization-induced dependencies, but it appears incremental as it builds on existing deep learning and quantile regression methods.
The authors tackled the problem of modeling and forecasting multiple yield curves by introducing a deep learning model that combines self-attention and nonparametric quantile regression to generate point and interval forecasts while avoiding quantile crossing issues. Numerical experiments on two datasets confirmed the effectiveness of their approach.
This manuscript introduces deep learning models that simultaneously describe the dynamics of several yield curves. We aim to learn the dependence structure among the different yield curves induced by the globalization of financial markets and exploit it to produce more accurate forecasts. By combining the self-attention mechanism and nonparametric quantile regression, our model generates both point and interval forecasts of future yields. The architecture is designed to avoid quantile crossing issues affecting multiple quantile regression models. Numerical experiments conducted on two different datasets confirm the effectiveness of our approach. Finally, we explore potential extensions and enhancements by incorporating deep ensemble methods and transfer learning mechanisms.