Rebuilding the limit order book: sequential Bayesian inference on hidden states

Abstract

The limit order book of an exchange represents an information store of market participants' future aims and for many traders the information held in this store is of interest. However, information loss occurs between orders being entered into the exchange and limit order book data being sent out. We present an online algorithm which carries out Bayesian inference to replace information lost at the level of the exchange server and apply our proof of concept algorithm to real historical data from some of the world's most liquid futures contracts as traded on CME GLOBEX, EUREX and NYSE Liffe exchanges.

Stochastic simulation framework for the limit order book using liquidity-motivated agents

“…The second approach to LOB modelling considers pure stochastic model frameworks, see for instance Christensen et al [2013]. This approach abstracts away the market participant from the modelling process.…”

“…These models can capture key empirical properties of the processes comprising the LOB stochastic structure [Cont et al, 2010, Huang andKercheval, 2012]. They also give rise to LOB simulation frameworks which feature these same properties, see for instance Christensen et al [2013], Daniels et al [2003].…”