Upcoming Talks
Upcoming Talks
(16:00 UTC) = (11:00 New York) = (17:00 Paris) = (00:00 Shanghai) = (03:00 Sydney)
Tail calibration of probabilistic forecasts
Abstract: Probabilistic forecasts comprehensively describe the uncertainty in the unknown future outcome, making them essential for decision making and risk management. While several methods have been introduced to evaluate probabilistic forecasts, existing evaluation techniques are ill-suited to the evaluation of tail properties of such forecasts. However, these tail properties are often of particular interest to forecast users due to the severe impacts caused by extreme outcomes. In this work, we introduce a general notion of tail calibration for probabilistic forecasts, which allows forecasters to assess the reliability of their predictions for extreme outcomes. We study the relationships between tail calibration and standard notions of forecast calibration, and discuss connections to peaks-over-threshold models in extreme value theory. Diagnostic tools are introduced and applied in a case study on European precipitation forecasts.
(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.