A Neuro-Genetic Algorithm for Heteroskedastic Time-Series Processes Empirical Tests on Global Asset Returns

Abstract

 The paper proposes a new neuro-genetic hybrid algorithm (NGHA) for coping with ill-conditioned time-series processes. Extensive testing and comparisons to various heteroskedastic models indicate that the neuro-genetic algorithm may be a useful device for modelling complicated time series. NGHA is used to model a factor price series corresponding to the European factor of a representative set of global asset returns. NGHA provides a platform for adapting evolutionary computation to the search for suitable networks for observed time series.