SMC-ABC methods for the estimation of stochastic simulation models of the limit order book

TL;DR

This paper introduces SMC-ABC methods to calibrate complex stochastic models of the Limit Order Book, enabling better analysis of financial market dynamics from high-dimensional data.

Contribution

It develops a stochastic modeling framework for the Limit Order Book and demonstrates how to calibrate these models to real data using SMC-ABC techniques.

Findings

Effective calibration of LOB models to real data.

Enhanced understanding of stochastic dynamics in financial markets.

Potential for improved trading strategies and risk management.

Abstract

In this paper we consider classes of models that have been recently developed for quantitative finance that involve modelling a highly complex multivariate, multi-attribute stochastic process known as the Limit Order Book (LOB). The LOB is the primary data structure recorded each day intra-daily for all assets on every electronic exchange in the world in which trading takes place. As such, it represents one of the most important fundamental structures to study from a stochastic process perspective if one wishes to characterize features of stochastic dynamics for price, volume, liquidity and other important attributes for a traded asset. In this paper we aim to adopt the model structure which develops a stochastic model framework for the LOB of a given asset and to explain how to perform calibration of this stochastic model to real observed LOB data for a range of different assets.