  A quasi-centralized limit order book (QCLOB) is a limit order book (LOB) in
which financial institutions can only access the trading opportunities offered
by counterparties with whom they possess sufficient bilateral credit. We
perform an empirical analysis of a recent, high-quality data set from a large
electronic trading platform that utilizes QCLOBs to facilitate trade. We find
many significant differences between our results and those widely reported for
other LOBs. We also uncover a remarkable empirical universality: although the
distributions describing order flow and market state vary considerably across
days, a simple, linear rescaling causes them to collapse onto a single curve.
Motivated by this finding, we propose a semi-parametric model of order flow and
market state in a QCLOB on a single trading day. Our model provides similar
performance to that of parametric curve-fitting techniques, while being simpler
to compute and faster to implement.
