Copulas

Copulas have become a central tool in modern multivariate statistics, offering a flexible framework to model and analyze complex dependence structures beyond the limitations of classical correlation. By separating the marginal behavior of random variables from their dependence structure, copulas allow researchers to capture non-linear, asymmetric, and tail dependencies that often occur in real-world data. The session will cover both theoretical advances and practical applications of copula models. Topics include novel approaches to copula construction and estimation, dependence measures, inference procedures, and goodness-of-fit testing. Special emphasis will be placed on recent developments in high-dimensional settings, time-series contexts, and dynamic copula models.

Organized by:

Ostap Okhrin (Germany)