DeepAI AI Chat
Log In Sign Up

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

12/18/2018
by   Francisco Blasques, et al.
University of Economics, Prague (Vysoká škola ekonomická v Praze)
Vrije Universiteit Amsterdam
0

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 decimal places in the proposed model is less severe than incorrect treatment of zero values in continuous models.

READ FULL TEXT

page 1

page 2

page 3

page 4

08/29/2023

Parametric quantile autoregressive conditional duration models with application to intraday value-at-risk

The modeling of high-frequency data that qualify financial asset transac...
07/04/2023

Asymptotics for the Generalized Autoregressive Conditional Duration Model

Engle and Russell (1998, Econometrica, 66:1127–1162) apply results from ...
03/31/2020

Clustering of Arrivals in Queueing Systems: Autoregressive Conditional Duration Approach

Arrivals in queueing systems are typically assumed to be independent and...
05/19/2020

A Flexible Stochastic Conditional Duration Model

We introduce a new stochastic duration model for transaction times in as...