发表机构
Prague University of Economics and Business（布拉格经济大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高频外汇交易持续期在整数值附近堆积的现象，提出GA-ACD模型，通过两分量混合分布捕捉堆积效应，实证表明该模型优于标准广义伽马ACD模型。
AI 中文摘要
高频外汇数据中的交易持续期呈现出在整数值附近出现频率升高的现象。为解决这一实证现象，我们提出了粒度调整自回归条件持续期（GA-ACD）模型。该模型基于一种新型两分量混合分布，其中一个分量为用于常规持续期的标准广义伽马分布，另一个分量则在整数值附近局部重新分配概率质量以捕捉堆积效应。条件动态在得分驱动框架内建模，使尺度参数能根据过去的持续期随时间变化，并支持对所有模型参数进行最大似然估计。模拟研究表明，忽略堆积效应会导致参数估计有偏，且对持续期的分布和动态的推断出现偏差。实证分析显示，整数持续期聚类在主要货币对中普遍存在，且GA-ACD模型的表现优于标准广义伽马ACD模型。
英文摘要
Trade durations in high-frequency foreign exchange data exhibit increased occurrence near integer values. To address this empirical phenomenon, we propose the granularity-adjusted autoregressive conditional duration (GA-ACD) model. It is based on a novel two-component mixture distribution consisting of a standard generalized gamma component for regular durations and a second component that locally redistributes probability mass around integer values to capture heaping. Conditional dynamics are modeled within a score-driven framework, allowing the scale parameter to vary over time in response to past durations, and enabling maximum likelihood estimation of all model parameters. A simulation study shows that ignoring heaping leads to biased parameter estimates and distorted inference regarding both the distribution and the dynamics of durations. An empirical analysis demonstrates that integer-duration clustering is pervasive across major currency pairs and that the GA-ACD model outperforms the standard generalized gamma ACD model.
