Recent Advances in Conditional Extreme Quantile Analysis

Estimating conditional extreme quantiles is essential for assessing tail risks in complex systems, with broad applications in finance, climate science, engineering, and beyond. While classical extreme value theory provides a foundational framework, recent advances, particularly semiparametric and nonparametric methods, including approaches based on quantile regression, machine learning, and deep learning, have greatly enriched the methodological landscape. This modern review synthesizes these developments, covering traditional likelihood-based methods, semiparametric approaches, and tree-based and deep learning techniques, including higher-order refinements.