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. ∗For constructive comments and suggestions we thank Eric Renault and an anonymous referee, the participants of the 2011 European Meeting of the Econometric Society, the 2011 Humboldt-Copenhagen Conference on Financial Econometrics and workshops at Humboldt-Universitat zu Berlin. This research is supported by the Deutsche Forschungsgemeinschaft (DFG) via the Collaborative Research Center 649 ”Economic Risk”. †Corresponding author. Institute for Statistics and Econometrics and Center for Applied Statistics and Economics (CASE), Humboldt-Universitat zu Berlin, as well as Center for Financial Studies (CFS), Frankfurt. Email: nikolaus.hautsch@wiwi.hu-berlin.de. Address: Spandauer Str. 1, D-10178 Berlin, Germany. ‡Institute for Statistics and Econometrics, Humboldt-Universitat zu Berlin. Email: malecpet@hu-berlin.de. Address: Spandauer Str. 1, D-10178 Berlin, Germany. §Institute for Statistics and Econometrics and Center for Applied Statistics and Economics (CASE), HumboldtUniversitat zu Berlin. Email: melanie.schienle@wiwi.hu-berlin.de. Address: Spandauer Str. 1, D-10178 Berlin, Germany.
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