Equity Market Impact Modeling: an Empirical Analysis for Chinese Market

TL;DR

This paper develops an empirical price impact model tailored for the Chinese stock market, accounting for heteroscedasticity and impact dependencies, and demonstrates its superiority over existing models using extensive trade data.

Contribution

The paper introduces a novel impact model considering heteroscedasticity and impact dependency, validated with large-scale Chinese market data, improving upon previous models like Almgren's.

Findings

Model outperforms Almgren's model in accuracy.

Impact function exponent $eta$ is approximately 0.7.

Parameter $eta$ remains constant across stocks.

Abstract

Market impact has become a subject of increasing concern among academics and industry experts. We put forward a price impact model which considers the heteroscedasticity of price in the time dimension and dependency between permanent impact and temporary impact. We discuss and derive the extremum of the expectation of permanent impact and realized impact by constructing several special trading trajectories. Given our use of a large trade and quote tick records of 17,213,238,343 compiled from the Chinese stock market, the model assessment ultimately suggest that our model is better than Almgren's model. Interestingly, the result of random effect analysis indicates the parameter $\alpha$, which is the exponent of the impact function, is a constant with a value of around 0.7 across all stocks. Our model and empirical result would give academia some insight of mechanism of Chinese market, and can be applied to algorithm trading.