Adaptive forecasting of the EURIBOR swap term structure

In this paper we adopt a principal components analysis (PCA) to reduce the dimensionality of the term structure and employ autoregressive (AR) models to forecast principal components which, in turn, are used to forecast swap rates. Arguing in favour of structural variation, we propose data‐driven, adaptive model selection strategies based on the PCA/AR model. To evaluate ex ante forecasting performance for particular rates, distinct forecast features, such as mean squared errors, directional accuracy and directional forecast value, are considered. It turns out that, relative to benchmark models, the adaptive approach offers additional forecast accuracy in terms of directional accuracy and directional forecast value.