利用超高频数据基于不规则时间序列对知情交易概率和交易活跃程度进行系统建模,使用Hamilton状态转移方程和混沌优化算法对超高频知情交易概率进行估计,实证检验中国股票市场上交易持续期间、交易量和知情交易概率之间的日内动态关系及其相互影响,重点研究交易活跃程度和信息之间的超高频特性。结果表明在超高频水平上久期和交易量之间呈现负相关关系,并且信息冲击会导致交易更活跃。
This paper presents a framework to model the Informed Trading Probability and trading activity by using the High Frequency Irregularly Spaced Transaction Data. The state-transition equation and chaos optimization algorithm are applied to estimate the High Frequency Informed Trading Probability. We empirically test the intraday dynamic relationship between trade duration, volume and trading activity and their interactions, and find that trade duration and volume are negatively correlated, and the impact of information will lead to more active stock traded.