Modeling of the German yield curve by error correction neural networks

Abstract

In this paper we introduce a new recurrent network architecture called ECNN, which includes the last model error measured as an additional input. Hence, the learning can interpret the models misfit as an external shock which can be used to guide the model dynamics afterwards. As extentions to the ECNN, we present a concept called overshooting, which enforces the autoregressive part of the model, and we combine our approach with a bottleneck coordinate transformation to handle high dimensional problems (variants-invariants separation). Finally we apply the ECNN to the German yield curve. Our model allows a forecast of ten different interest rate maturities on forecast horizons between one and six months ahead. It turns out, that our approach is superior to more conventional forecasting techniques.