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On a quantile autoregressive conditional duration model

Author

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  • Saulo, Helton
  • Balakrishnan, Narayanaswamy
  • Vila, Roberto

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 work, 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 yields better fit of the tails than the traditional Birnbaum–Saunders distribution, in addition to facilitating 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 the evaluation of a form of residuals. Two real financial transaction data sets are finally analyzed to illustrate the proposed approach.

Suggested Citation

  • Saulo, Helton & Balakrishnan, Narayanaswamy & Vila, Roberto, 2023. "On a quantile autoregressive conditional duration model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 203(C), pages 425-448.
    • Handle: RePEc:eee:matcom:v:203:y:2023:i:c:p:425-448
    DOI: 10.1016/j.matcom.2022.06.032
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    References listed on IDEAS

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