  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.
