Brexit Risk Implied by the SABR Martingale Defect in the EUR-GBP Smile

TL;DR

This paper introduces a Bayesian approach to quantify Brexit-related tail risk in the EUR-GBP option market by analyzing the SABR model's martingale defect, providing a new data-driven risk indicator.

Contribution

It develops a closed-form expression for the SABR martingale defect and employs Bayesian methods to estimate uncertainty, enhancing tail risk measurement during Brexit events.

Findings

The martingale defect correlates with Brexit-related tail risk.

Bayesian estimation provides credible uncertainty bounds.

The Brexit 'fever curve' illustrates market risk dynamics in 2019.

Abstract

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.