Abstract Inference in extreme value theory (EVT) relies on a limited number of extreme observations, making estimation challenging. To address this limitation, we propose a nonparametric simulation scheme, the multivariate extreme events spectral bootstrap simulation procedure, relying on the spectral representation of multivariate generalized Pareto‐distributed random vectors. Unlike standard bootstrap methods, our approach preserves the joint tail behavior of the data and generates additional synthetic extreme data, thereby improving the reliability of inference. We demonstrate the effectiveness of our procedure on the estimation of tail risk metrics, under both simulated and real data. The results highlight the potential of this method for enhancing risk assessment in high‐dimensional extreme scenarios.
Madhar et al. (Sun,) studied this question.