Extreme Conditional Quantile Estimation in High Dimensions: A Comparative Study

Summary This paper addresses the problem of estimating extreme conditional quantiles in high‐dimensional settings. We mainly focus on the case where the conditional distribution is heavy tailed. We consider recent estimation procedures based on machine learning techniques, including neural networks and random forests, as well as dimension reduction approaches specifically designed for extreme values. A comprehensive simulation study evaluates their performance across various scenarios, investigating the influence of the covariate dimension, the second‐order tail behaviour, the complexity of the link between covariate and tail index, the correlation structure of the random covariate and the choice of the intermediate quantile level. Our findings provide practical guidelines for selecting appropriate methods and highlight the strengths and limitations of each approach.