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

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. © 2013 © 2013 Taylor & Francis.

The meta-converse bound is tight

We show that the meta-converse bound derived by Polyanskiy et al. provides the exact error probability for a fixed joint source-channel code and an appropriate choice of the bound parameters. While the expression is not computable in general, it identifies the weaknesses of known converse bounds to the minimum achievable error probability. © 2013 IEEE.