Our main research questions concern how high-frequency (intraday) returns improve our understanding of extreme events’ dynamic properties and how realized measures of extreme events could be valuable predictors of the occurrence, magnitude, and tail dependence of future extreme events. Further, how high-frequency data facilitate the modeling and forecasting of systemic tail risk and how extreme events of high-frequency trading are related to the strength of spatial dependence and financial stability. This research project will pursue three main objectives: i) to develop an econometric framework for systemic tail risk modeling where the dynamic of extreme events in financial markets at high frequency will play an important role, ii) to propose a novel multivariate framework with realized measures based on information obtained from intraday high-frequency extreme events, iii) to study the relationship between extremes and financial spatial dependence in financial markets.