Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/56629
Authors: 
Hautsch, Nikolaus
Malec, Peter
Schienle, Melanie
Year of Publication: 
2010
Series/Report no.: 
SFB 649 discussion paper 2010-055
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 on high frequencies, such as cumulated trading volumes or the time between potentially simultaneously occurring market events. 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 liquid NYSE stocks, we show that the model captures both the dynamic and distribution properties of the data very 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
Document Type: 
Working Paper

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