"Microstructure Modes" -- Disentangling the Joint Dynamics of Prices & Order Flow

TL;DR

This paper analyzes the joint dynamics of prices and order flow in electronic order books using a novel double coarse-graining method and principal component analysis to identify key microstructure modes, revealing stable dynamics and insights into liquidity behavior.

Contribution

It introduces a double coarse-graining procedure and a PCA-based approach to extract and analyze microstructure modes, providing a new framework for understanding price and flow dynamics.

Findings

VAR model parameters are highly stable over time.

Symmetric liquidity modes have high prediction accuracy.

Long-memory effects suggest potential for endogenous liquidity crises.

Abstract

Understanding the micro-dynamics of asset prices in modern electronic order books is crucial for investors and regulators. In this paper, we use an order by order Eurostoxx database spanning over 3 years to analyze the joint dynamics of prices and order flow. In order to alleviate various problems caused by high-frequency noise, we propose a double coarse-graining procedure that allows us to extract meaningful information at the minute time scale. We use Principal Component Analysis to construct "microstructure modes" that describe the most common flow/return patterns and allow one to separate them into bid-ask symmetric and bid-ask anti-symmetric. We define and calibrate a Vector Auto-Regressive (VAR) model that encodes the dynamical evolution of these modes. The parameters of the VAR model are found to be extremely stable in time, and lead to relatively high $R^2$ prediction scores, especially for symmetric liquidity modes. The VAR model becomes marginally unstable as more lags are included, reflecting the long-memory nature of flows and giving some further credence to the possibility of "endogenous liquidity crises". Although very satisfactory on several counts, we show that our VAR framework does not account for the well known square-root law of price impact.