English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Autoregressive conditional duration (ACD) models are primarily used to dealwith data arising from times between two successive events. These models areusually specified in terms of a time-varying conditional mean or medianduration. In this paper, we relax this assumption and consider a conditionalquantile approach to facilitate the modeling of different percentiles. Theproposed ACD quantile model is based on a skewed version of Birnbaum-Saundersdistribution, which provides better fitting of the tails than the traditionalBirnbaum-Saunders distribution, in addition to advancing the implementation ofan expectation conditional maximization (ECM) algorithm. A Monte Carlosimulation study is performed to assess the behavior of the model as well asthe parameter estimation method and to evaluate a form of residual. A realfinancial transaction data set is finally analyzed to illustrate the proposedapproach.

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