Finite Mixture of ARMA-GARCH Model for Stock Price Prediction

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 ARMA-GARCH 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.

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