Quasi-Centralized Limit Order Books

TL;DR

This paper analyzes quasi-centralized limit order books (QCLOBs), revealing universal patterns in order flow after rescaling and proposing a semi-parametric model that simplifies and accelerates market state prediction.

Contribution

It uncovers empirical universality in QCLOB data and introduces a semi-parametric model that matches parametric methods in performance but is easier to compute.

Findings

Order flow and market state distributions collapse onto a single curve after rescaling.

Significant differences between QCLOBs and other LOBs are observed.

The proposed model performs comparably to parametric techniques with greater simplicity.

Abstract

A quasi-centralized limit order book (QCLOB) is a limit order book (LOB) in which financial institutions can only access the trading opportunities offered by counterparties with whom they possess sufficient bilateral credit. We perform an empirical analysis of a recent, high-quality data set from a large electronic trading platform that utilizes QCLOBs to facilitate trade. We find many significant differences between our results and those widely reported for other LOBs. We also uncover a remarkable empirical universality: although the distributions describing order flow and market state vary considerably across days, a simple, linear rescaling causes them to collapse onto a single curve. Motivated by this finding, we propose a semi-parametric model of order flow and market state in a QCLOB on a single trading day. Our model provides similar performance to that of parametric curve-fitting techniques, while being simpler to compute and faster to implement.