Estimating returns and condicional volatility: a comparison between the ARMA-GARCH-M Models and the Backpropagation Neural Network

Abstract

Econometric models have usually estimated both returns and conditional volatility in financial assets. This paper is intended in the comparison of this traditional approach with the more recent Backpropagation neural network. When applied to the Spanish Ibex-35 stock market index, we find that the neural network achieved significantly better performance in predicting conditional volatility, but similar results when predicting financial returns.