Capturing the Zero: A New Class of Zero-Augmented Distributions and Multiplicative Error Processes

Hautsch, Nikolaus; Malec, Peter; Schienle, Melanie

doi:10.1093/jjfinec/nbt002

Cited by 52 publications

(48 citation statements)

References 42 publications

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“…It is successfully applied to financial duration data, where it was originally introduced by Engle and Russell () in the context of an autoregressive conditional duration (ACD) model. Likewise, it is applied to model intra‐day trading volumes, see, among others, Manganelli (); Brownlees et al (); Hautsch et al (). MEM parameters are typically estimated over long estimation windows in order to increase estimation efficiency.…”

Section: Introductionmentioning

confidence: 99%

Local Adaptive Multiplicative Error Models for High-Frequency Forecasts

2014

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Section: Introductionmentioning

confidence: 99%

Local Adaptive Multiplicative Error Models for High-Frequency Forecasts

2014

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“…On high-frequency time scales, (aggregate) order flow volumes are known to have a positive probabilistic mass at zero, i. e., there is a significant non-zero probability that no orders arrive over short horizons (see e.g., Hautsch et al, 2015). This is particularly relevant for less actively traded and less liquid stocks.…”

Section: Order Arrival and Trade Dynamicsmentioning

confidence: 99%

Optimal order display in limit order markets with liquidity competition

Cebiroğlu

Horst

2015

Journal of Economic Dynamics and Control

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“…Another way of incorporating zero durations in a model is to directly include excessive zero values in the underlying distribution. For continuous distributions, zero-augmented models proposed by Hautsch et al (2014) can be used. 1 .…”

Section: Introductionmentioning

confidence: 99%

Zero-Inflated Autoregressive Conditional Duration Model for Discrete Trade Durations With Excessive Zeros

2018

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In finance, durations between successive transactions are usually modeled by the autoregressive conditional duration model based on a continuous distribution omitting frequent zero values. Zero durations can be caused by either split transactions or independent transactions. We propose a discrete model allowing for excessive zero values based on the zero-inflated negative binomial distribution with score dynamics. We establish the invertibility of the score filter. Additionally, we derive sufficient conditions for the consistency and asymptotic normality of the maximum likelihood of the model parameters. In an empirical study of DJIA stocks, we find that split transactions cause on average 63% of zero values. Furthermore, the loss of decimal places in the proposed model is less severe than incorrect treatment of zero values in continuous models.

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Capturing the Zero: A New Class of Zero-Augmented Distributions and Multiplicative Error Processes

Cited by 52 publications

References 42 publications

Local Adaptive Multiplicative Error Models for High-Frequency Forecasts

Local Adaptive Multiplicative Error Models for High-Frequency Forecasts

Optimal order display in limit order markets with liquidity competition

Zero-Inflated Autoregressive Conditional Duration Model for Discrete Trade Durations With Excessive Zeros

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