Multiple Yield Curve Modeling and Forecasting using Deep Learning
Ronald Richman Salvatore Scognamiglio
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.