On a quantile autoregressive conditional duration model applied to high-frequency financial data

TL;DR

This paper introduces a quantile autoregressive conditional duration model for high-frequency financial data, using a skewed Birnbaum-Saunders distribution and an ECM algorithm to better model tail behavior and different percentiles.

Contribution

It extends traditional ACD models by incorporating a quantile approach with a skewed distribution and develops an ECM algorithm for improved estimation.

Findings

The model effectively captures tail behavior in financial durations.

Simulation studies validate the estimation method's accuracy.

Application to real data demonstrates practical utility.

Abstract

Autoregressive conditional duration (ACD) models are primarily used to deal with data arising from times between two successive events. These models are usually specified in terms of a time-varying conditional mean or median duration. In this paper, we relax this assumption and consider a conditional quantile approach to facilitate the modeling of different percentiles. The proposed ACD quantile model is based on a skewed version of Birnbaum-Saunders distribution, which provides better fitting of the tails than the traditional Birnbaum-Saunders distribution, in addition to advancing the implementation of an expectation conditional maximization (ECM) algorithm. A Monte Carlo simulation study is performed to assess the behavior of the model as well as the parameter estimation method and to evaluate a form of residual. A real financial transaction data set is finally analyzed to illustrate the proposed approach.