ABSTRACT

In the literature, the finite mixture of autoregressive (AR), finite mixture of autoregressive moving average (ARMA) and finite mixture of autoregressive generalized autoregressive conditional heteroscedasticity (AR-GARCH) models have been respectively adopted for finance exchange rate prediction. In this paper, we consider to extend the mixture of AR-GARCH model (W.C. Wong, F. Yip and L. Xu, 1998) to the mixture of ARMAGARCH model and investigate its application in stock price prediction. A generalized expectationmaximization (GEM) algorithm is proposed to learn the mixture model. Experimental simulations show that the mixture of ARMA-GARCH model yields better prediction results than either the mixture of AR, or the mixture of AR-GARCH models.