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arXiv · 2109.03844

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

Helton Saulo·Narayanaswamy Balakrishnan·Roberto Vila
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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.

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Helton Saulo, Narayanaswamy Balakrishnan, Roberto Vila. 2021-09-08. On a quantile autoregressive conditional duration model applied to high-frequency financial data. https://arxiv.org/abs/2109.03844

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