Time-Varying Mixing Weights in Mixture Autoregressive Conditional Duration Models
扩展了Engle和Russell的自回归条件持续时间模型，采用时变权重的混合分布方法，解决了标准模型在尾部行为建模上的不足，并基于市场活动数据对权重进行结构化解释。
Financial market price formation and exchange activity can be investigated by means of ultra-high frequency data. In this article, we investigate an extension of the Autoregressive Conditional Duration (ACD) model of Engle and Russell (1998 Engle , R. F. , Russell , J. R. ( 1998 ). Autoregressive conditional duration: a new model for irregularly spaced transaction data . Econometrica 66 : 1127 – 1162 .[Crossref], [Web of Science ®] , [Google Scholar]) by adopting a mixture of distribution approach with time-varying weights. Empirical estimation of the Mixture ACD model shows that the limitations of the standard base model and its inadequacy of modelling the behavior in the tail of the distribution are suitably solved by our model. When the weights are made dependent on some market activity data, the model lends itself to some structural interpretation related to price formation and information diffusion in the market.