WIT Press


Estimating Term Structure Of Interest Rates: Neural Network Vs One Factor Parametric Models

Price

Free (open access)

Paper DOI

10.2495/DATA030411

Volume

29

Pages

8

Published

2003

Size

351 kb

Author(s)

F. Abid & M. B. Salah

Abstract

Estimating term structure of interest rates: neural network vs one factor parametric models F. Abid & M. B. Salah Faculty of Economics and Busines, Sfax, Tunisia Abstract The aim of this paper is twofold; first we concentrate on the work of Vasicek (1977) and Cox, Ingersoll and Ross (1985). We examine and test empirically each model and discuss its performance in predicting the term structure of interest rates using a parametric estimating approach GMM (Generalized Moments Method). Second we estimate the term structure of interest rate dynamics using a nonparametric approach ANN (Artificial Neural Network). Two neural network models are performed. The first model uses spreads between interest rates of 10 different maturities as the only explanatory variable of interest rate changes. The second model introduces two factors, spreads and interest rates' levels. Using historical U.S. Treasury bill rates and Treasury bond yields, we compare the ability of each model to predict the term structure of

Keywords