The modeling of high-frequency data that qualify financial asset transactions has been an area of relevant interest among statisticians and econometricians -- above all, the analysis of time series of financial durations. Autoregressive conditional duration (ACD) models have been the main tool for modeling financial transaction data, where duration is usually defined as the time interval between two successive events. These models are usually specified in terms of a time-varying mean (or median) conditional duration. In this paper, a new extension of ACD models is proposed which is built on the basis of log-symmetric distributions reparametrized by their quantile. The proposed quantile log-symmetric conditional duration autoregressive model allows us to model different percentiles instead of the traditionally used conditional mean (or median) duration. We carry out an in-depth study of theoretical properties and practical issues, such as parameter estimation using maximum likelihood method and diagnostic analysis based on residuals. A detailed Monte Carlo simulation study is also carried out to evaluate the performance of the proposed models and estimation method in retrieving the true parameter values as well as to evaluate a form of residuals. Finally, the proposed class of models is applied to a price duration data set and then used to derive a semi-parametric intraday value-at-risk (IVaR) model.

翻译：高频数据建模（特别是描述金融资产交易的时间序列久期分析）一直是统计学家和经济计量学家关注的重要领域。自回归条件久期（ACD）模型是金融交易数据建模的主要工具，其中久期通常定义为两个连续事件之间的时间间隔。这类模型通常以时变条件均值（或中位数）久期的形式设定。本文提出了一种基于对数对称分布并通过分位数重新参数化的ACD模型新扩展。所提出的分位数对数对称条件久期自回归模型允许对不同的百分位数进行建模，而非传统使用的条件均值（或中位数）久期。我们对其理论性质和实际问题（如基于极大似然法的参数估计与基于残差的诊断分析）进行了深入研究。通过详细的蒙特卡洛模拟研究，评估了所提模型及估计方法在还原真实参数值方面的表现，并检验了一种残差形式。最后，将该类模型应用于价格久期数据集，进而推导出半参数日内风险价值（IVaR）模型。