A Stochastic Model of Order Book Dynamics using Bouncing Geometric Brownian Motions

TL;DR

This paper introduces a stochastic model of order book dynamics using bouncing geometric Brownian motions, revealing that trading times follow an inverse Gaussian distribution and that prices converge to Brownian motion under scaling.

Contribution

The paper develops a novel bouncing GBM model for order book prices, linking it to trading time distributions and demonstrating convergence to Brownian motion as the parameter approaches zero.

Findings

Inter-trading times are inverse Gaussian distributed.

Logarithmic returns follow a normal inverse Gaussian distribution.

The price process converges to a Brownian motion under scaling.

Abstract

We consider a limit order book, where buyers and sellers register to trade a security at specific prices. The largest price buyers on the book are willing to offer is called the market bid price, and the smallest price sellers on the book are willing to accept is called the market ask price. Market ask price is always greater than market bid price, and these prices move upwards and downwards due to new arrivals, market trades, and cancellations. We model these two price processes as "bouncing geometric Brownian motions (GBMs)", which are defined as exponentials of two mutually reflected Brownian motions. We then modify these bouncing GBMs to construct a discrete time stochastic process of trading times and trading prices, which is parameterized by a positive parameter $\delta$. Under this model, it is shown that the inter-trading times are inverse Gaussian distributed, and the logarithmic returns between consecutive trading times follow a normal inverse Gaussian distribution. Our main results show that the logarithmic trading price process is a renewal reward process, and under a suitable scaling, this process converges to a standard Brownian motion as $\delta\to 0$. We also prove that the modified ask and bid processes approach the original bouncing GBMs as $\delta\to0$. Finally, we derive a simple and effective prediction formula for trading prices, and illustrate the effectiveness of the prediction formula with an example using real stock price data.