In finance, durations between successive transactions are usually modeled by the autoregressive conditional duration model based on a continuous distribution omitting zero values. Zero or close-to-zero durations can be caused by either split transactions or independent transactions. We propose a discrete model allowing for excessive zero values based on the zero-inflated negative binomial distribution with score dynamics. This model allows to distinguish between the processes generating split and standard transactions. We use the existing theory on score models to establish the invertibility of the score filter and verify that sufficient conditions hold for the consistency and asymptotic normality of the maximum likelihood of the model parameters. In an empirical study, we find that split transactions cause between 92 and 98 percent of zero and close-to-zero values. Furthermore, the loss of decimal places in the proposed approach is less severe than the incorrect treatment of zero values in continuous models.
翻译:在金融方面,连续交易之间的持续时间通常以基于连续分配而省略零值的自动递减性有条件期限模式为模型。 零期或接近零期期可以由拆分交易或独立交易造成。 我们提出了一个允许基于零膨胀负二进制分布和分数动态的超零值的离散模式。 这个模式可以区分产生拆分和标准交易的过程。 我们使用分数模式的现有理论来确定分数过滤器的可忽略性,并核实是否有足够的条件使模型参数的最大可能性具有一致性和无损正常性。 在一项经验研究中,我们发现,将零值和零值的零值和接近零值的零值分成92%至98%。此外,拟议方法中小数位的损失比连续模型中对零值的不正确处理要小。