  Many commonly used liquidity measures are based on snapshots of the state of
the limit order book (LOB) and can thus only provide information about
instantaneous liquidity, and not regarding the local liquidity regime. However,
trading in the LOB is characterised by many intra-day liquidity shocks, where
the LOB generally recovers after a short period of time. In this paper, we
capture this dynamic aspect of liquidity using a survival regression framework,
where the variable of interest is the duration of the deviations of the spread
from a pre-specified level. We explore a large number of model structures using
a branch-and-bound subset selection algorithm and illustrate the explanatory
performance of our model.
