Dynamic BivarSate Mixture Models: Modeling the Behavior of Prices and Trading Volume
估计了具有序列相关混合变量的动态双变量混合模型，用于解释金融市场上日价格变化与交易量的随机行为，并基于德国股市数据检验其能否解释价格变化方差的持续性。
Bivariate mixture models have been used to explain the stochastic behavior of daily price changes and trading volume on financial markets. In this class of models, price changes and volume follow a mixture of bivariate distributions with the unobservable number of price-relevant information serving as the mixing variable. The time series behavior of this mixing variable determines the dynamics of the price-volume system. In this article, bivariate mixture specifications with a serially correlated mixing variable are estimated by simulated maximum likelihood and analyzed concerning their ability to account for the observed dynamics on financial markets, especially the persistence in the variance of price changes. The results, based on German stock-market data, reveal that the dynamic bivariate mixture models cannot account for the persistence in the price-change variance.