This talk will address the problem of high quantile estimation conditional on a related variable being extreme. The problem set-up is of interest in a number applications to evaluate tail risk of a focal variable in the tail of a conditioning variable. A primary example is the assessment of systemic risk in financial markets using a risk measure known as the conditional value-at-risk (CoVaR). The proposed estimator is based on a novel approach to handle the bivariate tail dependence structure through an adjustment factor that can be used in conjunction with univariate high quantile estimation techniques. We establish the asymptotic behaviour of the estimator under relatively weak assumptions, and illustrate its performance via simulation studies and a real data example.

Bio: Dr. Natalia Nolde is a Professor in the Department of Statistics at the University of British Columbia and a Canada Research Chair (Tier I) in Statistics of Extremes and Risk. She obtained her PhD in Mathematics from ETH Zurich in 2010. Her research focuses on probabilistic and statistical aspects of multivariate extreme value modelling, with applications to risk management in finance, insurance, hydrology and geoscience.

The paper has recently been accepted and there is an author version available online: https://www.tandfonline.com/doi/full/10.1080/01621459.2026.2640643