Capturing the zero: A new class of zero-augmented distributions and multiplicative error processes

Nikolaus Hautsch, Peter Malec and Melanie Schienle

No 2010-055, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

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.

Keywords: high-frequency data; point-mass mixture; multiplicative error model; excess zeros; semiparametric specification test; market microstructure (search for similar items in EconPapers)
JEL-codes: C14 C16 C22 C25 C51 (search for similar items in EconPapers)
Date: 2010
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Related works:
Journal Article: Capturing the Zero: A New Class of Zero-Augmented Distributions and Multiplicative Error Processes (2014)
Journal Article: Capturing the Zero: A New Class of Zero-Augmented Distributions and Multiplicative Error Processes (2013)
Working Paper: Capturing the zero: A new class of zero-augmented distributions and multiplicative error processes (2011)
Working Paper: Capturing the zero: A new class of zero-augmented distributions and multiplicative error processes (2010)
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