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

Helton Saulo, Narayanaswamy Balakrishnan and Roberto Vila

Papers from arXiv.org

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.

Date: 2021-09
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-isf
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