Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/57371 
Year of Publication: 
2011
Series/Report no.: 
CFS Working Paper No. 2011/25
Publisher: 
Goethe University Frankfurt, Center for Financial Studies (CFS), Frankfurt a. M.
Abstract: 
We propose a novel approach to model serially dependent positive-valued variables which realize a non-trivial proportion of zero outcomes. This is a typical phenomenon in financial time series observed at high frequencies, such as cumulated trading volumes. We introduce a flexible point-mass mixture distribution and develop a semiparametric specification test explicitly tailored for such distributions. Moreover, we propose a new type of multiplicative error model (MEM) based on a zero-augmented distribution, which incorporates an autoregressive binary choice component and thus captures the (potentially different) dynamics of both zero occurrences and of strictly positive realizations. Applying the proposed model to high-frequency cumulated trading volumes of both liquid and illiquid NYSE stocks, we show that the model captures the dynamic and distributional properties of the data well and is able to correctly predict future distributions.
Subjects: 
High-Frequency Data
Point-Mass Mixture
Multiplicative Error Model
Excess Zeros
Semiparametric Specification Test
Market Microstructure
JEL: 
C22
C25
C14
C16
C51
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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