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

TL;DR

This paper introduces a zero-inflated autoregressive model for discrete trade durations that effectively accounts for excessive zeros, distinguishing between split and standard transactions, and demonstrates improved handling of zero durations in financial data.

Contribution

The paper proposes a novel zero-inflated negative binomial model with score dynamics for discrete durations, addressing limitations of continuous models in handling zero values.

Findings

Split transactions account for 92-98% of zeros.

The proposed model reduces loss of decimal precision.

Model confirms zero inflation is significant in trade durations.

Abstract

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.