Brexit news propagation in financial systems: multidimensional visibility networks for market volatility dynamics

In this paper, we propose a multivariate procedure based on multidimensional visibility graphs to detect changes in the market volatility of UK financial indices, considered both before and after Brexit main events. We produce a graph-theoretical representation of volatility time series derived from equity indexes, government rates and currencies to investigate the behavior of the aggregate market volatility through the use of global centrality measures. By employing a stylized agent-based model, we show that the proposed approach is able to discriminate between periods of high and low volatility, both in the temporal dimension and cross-sectionally among multiple time series. We aim at recognizing whether external news related to the Brexit process could induce significant ‘after-shocks’ (and also ‘pre-shocks’) in the system, by producing dynamic relaxation in the values of centrality measures, in line with the cascade effects described by the Omori earthquake law. In particular, high volatility cascades dissipate into the market via power-law relaxation. When compared with other categories of events, such as Bank of England monetary policy announcements, we observe significant market inefficiency in processing Brexit related news. We also find that strong market surprise related to specific Brexit news or a correct discount of some Brexit announcements can produce an inverse Omori law exhibiting convex relaxation.