Term Structure of Interest Rates Based on Artificial Neural Network

摘要

In light of the nonlinear approaching capability of artificial neural networks (ANN) , the term structure of interest rates is predicted using The generalized regression neural network (GRNN) and back propagation (BP) neural networks models. The prediction performance is measured with US interest rate data. Then, RBF and BP models are compared with Vasicek's model and Cox-Ingersoll-Ross (CIR) model. The comparison reveals that neural network models outperform Vasicek's model and CIR model, which are more precise and closer to the real market situation.