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

arXiv: https://arxiv.org/abs/2109.03844 · HTML: https://mlsanity.com/paper/2109.03844/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2021.

## Built on by (most cited first, 1 of 1)

- [Parametric quantile autoregressive conditional duration models with application to intraday value-at-risk](https://mlsanity.com/paper/2308.15571/) (2023)

## Abstract (arXiv)

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.