Endogeneous Dynamics of Intraday Liquidity

TL;DR

This paper analyzes the endogenous information in intraday liquidity variables across global equity markets using autoregressive models, revealing intrinsic dynamics that can inform trading strategies and serve as benchmarks.

Contribution

It provides a comprehensive empirical analysis of liquidity variable dynamics at five-minute intervals across multiple markets, highlighting endogenous effects without relying on external information.

Findings

Autoregressive models explain a significant portion of liquidity variable variations.

Differences in model performance across stocks and markets are observed.

The study uncovers autocorrelations in liquidity variables, challenging i.i.d. assumptions in trading models.

Abstract

In this paper we investigate the endogenous information contained in four liquidity variables at a five minutes time scale on equity markets around the world: the traded volume, the bid-ask spread, the volatility and the volume at first limits of the orderbook. In the spirit of Granger causality, we measure the level of information by the level of accuracy of linear autoregressive models. This empirical study is carried out on a dataset of more than 300 stocks from four different markets (US, UK, Japan and Hong Kong) from a period of over five years. We discuss the obtained performances of autoregressive (AR) models on stationarized versions of the variables, focusing on explaining the observed differences between stocks. Since empirical studies are often conducted at this time scale, we believe it is of paramount importance to document endogenous dynamics in a simple framework with no addition of supplemental information. Our study can hence be used as a benchmark to identify exogenous effects. On the other hand, most optimal trading frameworks (like the celebrated Almgren and Chriss one), focus on computing an optimal trading speed at a frequency close to the one we consider. Such frameworks very often take i.i.d. assumptions on liquidity variables; this paper document the auto-correlations emerging from real data, opening the door to new developments in optimal trading.