Normal mixture method for stock daily returns over different sub-periods

In this paper, the normal mixture model, as an alternative distribution, is utilized to represent the characteristics of stock daily returns over different bull and bear markets. Firstly, we conduct the normality test for the return data and compare the Kolmogorov-Smirnov statistics of normal mixture models with different components. Secondly, we analyze the likely reasons why parameters change over different sub-periods. Our empirical examination proves that majority of the data series reject the normality assumption and mixture models with three components can model the behavior of daily returns more appropriately and steadily. This result has both statistical and economic significance.