  We construct a data-driven statistical indicator for quantifying the tail
risk perceived by the EURGBP option market surrounding Brexit-related events.
We show that under lognormal SABR dynamics this tail risk is closely related to
the so-called martingale defect and provide a closed-form expression for this
defect which can be computed by solving an inverse calibration problem. In
order to cope with the the uncertainty which is inherent to this inverse
problem, we adopt a Bayesian statistical parameter estimation perspective. We
probe the resulting posterior densities with a combination of optimization and
adaptive Markov chain Monte Carlo methods, thus providing a careful uncertainty
estimation for all of the underlying parameters and the martingale defect
indicator. Finally, to support the feasibility of the proposed method, we
provide a Brexit "fever curve" for the year 2019.
