Time-varying Mixing Weights in Mixture Autoregressive Conditional Duration Models
Giovanni De Luca () and
Giampiero Gallo ()
Econometrics Working Papers Archive from Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti"
Financial market price formation and exchange activity can be investigated by means of ultra-high frequency data. In this paper we investigate an extension of the Autoregressive Conditional Duration (ACD) model of Engle and Russell (1998) 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.
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Journal Article: Time-Varying Mixing Weights in Mixture Autoregressive Conditional Duration Models (2009)
Working Paper: Time-varying Mixing Weights in Mixture Autoregressive Conditional Duration Models (2006)
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