The aim of this paper is twofold: first, we focus on the work of Vasicek (1977) and Cox, Ingersoll and Ross (1985). We investigate and test empirically for each model and discuss their results in the prediction of the term) interest rates with a parametric approach to estimate GMM (Generalized Method moments. Secondly, we consider the term structure dynamics interest rate parametric approach, ANN (Artificial Neural Network). Two implementation models of neural networks. The first model used the differences between rates of 10 different durations, as the only explanatory variable of changes in interest rates . The second model considers two factors in the spread of interests and all levels. Based on recent U.S. The rates of Treasury bonds and Treasury yields, we compare the ability of each model with the concept of interest can be predicted. The data are daily and cover the period from January 3, 1995, December 29, 2000. The results show that the neural network; Create Vasicek (1977) and Cox, Ingersoll and Ross (1985) model yield curves. The network models are more effective than standard parametric models. The prediction of success is achieved by two factors, the neural network model.
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