A Hawkes process analysis of high-frequency price endogeneity and market efficiency
用霍克斯过程分析中国股市108只股票的高频价格数据，发现价格变化存在显著内生性（平均分支比约0.81），且分支比越高，高频价格效率越低。
We use the Hawkes process to model the high-frequency price process of 108 stocks in the Chinese stock market, in order to understand the endogeneity of price changes and the mechanism of information processing. Using a piece-wise constant exogenous intensity, we employ non-parametric estimation, residual analysis, and Bayesian Information Criterion (BIC) to determine that a power-law kernel is the most appropriate for our data. We propose the internal branching ratio to represent endogeneity within a finite interval. The branching ratio tends to be higher after the market opens and before the market closes, with a mean value of around 0.81, suggesting significant endogeneity in price changes. In addition, we explore the relationship between branching ratios and stock characteristics using panel regression. Higher branching ratios are associated with lower levels of price efficiency at high, but not low, frequencies. Finally, the branching ratio increases over time without significant impact from COVID-19.