Order book model with herd behavior exhibiting long-range memory

TL;DR

This paper introduces a novel order book model incorporating herd behavior that captures long-range memory in financial data, validated against Bitcoin and NYSE data, and explores spectral density features related to price convergence.

Contribution

It combines empirical order book analysis with herd behavior modeling to replicate long-range memory in financial markets, a novel integration in the field.

Findings

Model replicates long-range memory of returns and trading activity.

Spectral density fracture linked to price convergence mechanisms.

Model aligns well with empirical Bitcoin and NYSE data.

Abstract

In this work, we propose an order book model with herd behavior. The proposed model is built upon two distinct approaches: a recent empirical study of the detailed order book records by Kanazawa et al. [Phys. Rev. Lett. 120, 138301] and financial herd behavior model. Combining these approaches allows us to propose a model that replicates the long-range memory of absolute returns and trading activity. We compare the statistical properties of the model against the empirical statistical properties of the Bitcoin exchange rates and New York stock exchange tickers. We also show that the fracture in the spectral density of the high-frequency absolute return time series might be related to the mechanism of convergence towards the equilibrium price.