Shanghai Shenzhen 300 INDEX Forecast With A New Neural Network GARCH Model

This paper introduces a nature-inspired intelligent model suitable for high-frequency financial time series. It combines a neural network parameterization for the mean with a linear (GARCH) parameterization for the variance. We propose a complete model building cycle for the family of NN-GARCH specifications that includes all three stages of econometric modeling (specification, estimation and evaluation). Based on the maximum likelihood theory, we device procedures for statistical inference in the framework of NN-GARCH models and thus offer the modeler the opportunity to test hypotheses of interest concerning both the mean and variance structure of the data-generating process. We demonstrate the model-building cycle by constructing an NN-GARCH dynamic model for the returns on the SHANGHAI SHENZHEN 300 INDEX.