A Hawkes process analysis of high-frequency price endogeneity and market efficiency

Abstract

ABSTRACT 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.