Next seminar: 

22 January 2026 - Axel Bücher (Ruhr University Bochum)

(15:00 UTC) = (10:00 New York) = (16:00 Paris) = (23:00 Shanghai) = (02:00 Sydney)

Latent linear factor models for tail dependence in high dimensions

Abstract: A common object to describe the extremal dependence of a d-variate random vector X is the stable tail dependence function L. Various parametric models have emerged, with a popular sub-class consisting of those stable tail dependence functions that arise for linear and max-linear factor models with heavy tailed factors. We study such models under the assumption that the factors are possibly dependent, which results in a model for L that depends on a (d x K) loading matrix A (with K << d the number of factors) and the lower-dimensional spectral measure of the K factors. We suggests algorithms to estimate K and A under an additional assumption on A called the ‘pure variable assumption’. The results are illustrated with numerical experiments and a case study.

Joint work with Alexis Boulin.

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