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

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  • Hautsch, Nikolaus
  • Malec, Peter
  • Schienle, Melanie

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. --

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Bibliographic Info

Paper provided by Center for Financial Studies (CFS) in its series CFS Working Paper Series with number 2011/25.

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Date of creation: 2011
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Handle: RePEc:zbw:cfswop:201125

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Keywords: High-Frequency Data; Point-Mass Mixture; Multiplicative Error Model; Excess Zeros; Semiparametric Specification Test; Market Microstructure;

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Citations

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Cited by:
  1. Basteck, Christian & Daniëls, Tijmen R., 2011. "Every symmetric 3×3 global game of strategic complementarities has noise-independent selection," Journal of Mathematical Economics, Elsevier, vol. 47(6), pages 749-754.
  2. Nikolaus Hautsch & Peter Malec & Melanie Schienle, 2010. "Capturing the Zero: A New Class of Zero-Augmented Distributions and Multiplicative Error Processes," SFB 649 Discussion Papers SFB649DP2010-055, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  3. Malec, Peter & Schienle, Melanie, 2014. "Nonparametric kernel density estimation near the boundary," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 72(C), pages 57-76.
  4. Wolfgang Karl Härdle & Nikolaus Hautsch & Andrija Mihoci, 2012. "Local Adaptive Multiplicative Error Models for High-Frequency Forecasts," SFB 649 Discussion Papers SFB649DP2012-031, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  5. Christos Kollias & Stephanos Papadamou & Costas Siriopoulos, 2013. "European Markets’ Reactions to Exogenous Shocks: A High Frequency Data Analysis of the 2005 London Bombings," International Journal of Financial Studies, MDPI, Open Access Journal, MDPI, Open Access Journal, vol. 1(4), pages 154-167, November.
  6. Nicole Wiebach & Lutz Hildebrandt, 2010. "Context Effects as Customer Reaction on Delisting of Brands," SFB 649 Discussion Papers SFB649DP2010-056, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  7. Ito, Ryoko, 2013. "Modeling dynamic diurnal patterns in high frequency financial data," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge 1315, Faculty of Economics, University of Cambridge.
  8. Enno Mammen & Christoph Rothe & Melanie Schienle, 2010. "Nonparametric Regression with Nonparametrically Generated Covariates," SFB 649 Discussion Papers SFB649DP2010-059, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  9. Andres, Philipp, 2014. "Maximum likelihood estimates for positive valued dynamic score models; The DySco package," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 76(C), pages 34-42.
  10. Trojan, Sebastian, 2014. "Modeling Intraday Stochastic Volatility and Conditional Duration Contemporaneously with Regime Shifts," Economics Working Paper Series 1425, University of St. Gallen, School of Economics and Political Science.

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