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

Fuente: arXiv
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Main Authors: Blasques, Francisco, Holý, Vladimír, Tomanová, Petra
Format: Preprint
Published: 2018
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author Blasques, Francisco
Holý, Vladimír
Tomanová, Petra
author_facet Blasques, Francisco
Holý, Vladimír
Tomanová, Petra
contents In finance, durations between successive transactions are usually modeled by the autoregressive conditional duration model based on a continuous distribution omitting zero values. Zero or close-to-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. This model allows to distinguish between the processes generating split and standard transactions. We use the existing theory on score models to establish the invertibility of the score filter and verify that sufficient conditions hold for the consistency and asymptotic normality of the maximum likelihood of the model parameters. In an empirical study, we find that split transactions cause between 92 and 98 percent of zero and close-to-zero values. Furthermore, the loss of decimal places in the proposed approach is less severe than the incorrect treatment of zero values in continuous models.
format Preprint
id arxiv_https___arxiv_org_abs_1812_07318
institution arXiv
publishDate 2018
record_format arxiv
spellingShingle Zero-Inflated Autoregressive Conditional Duration Model for Discrete Trade Durations with Excessive Zeros
Blasques, Francisco
Holý, Vladimír
Tomanová, Petra
Statistical Finance
Methodology
In finance, durations between successive transactions are usually modeled by the autoregressive conditional duration model based on a continuous distribution omitting zero values. Zero or close-to-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. This model allows to distinguish between the processes generating split and standard transactions. We use the existing theory on score models to establish the invertibility of the score filter and verify that sufficient conditions hold for the consistency and asymptotic normality of the maximum likelihood of the model parameters. In an empirical study, we find that split transactions cause between 92 and 98 percent of zero and close-to-zero values. Furthermore, the loss of decimal places in the proposed approach is less severe than the incorrect treatment of zero values in continuous models.
title Zero-Inflated Autoregressive Conditional Duration Model for Discrete Trade Durations with Excessive Zeros
topic Statistical Finance
Methodology
url https://arxiv.org/abs/1812.07318