EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Modeling Spatial Extremes Using Normal Mean-Variance Mixtures

Zhongwei Zhang1, Raphaël Huser1, Thomas Opitz2, and Jennifer Wadsworth3
Zhongwei Zhang et al.
  • 1CEMSE Division, King Abdullah University of Science and Technology, Saudi Arabia
  • 2Biostatistics and Spatial Processes, INRAE, France
  • 3Department of Mathematics and Statistics, Lancaster University, UK

Classical models for multivariate or spatial extremes are mainly based upon the asymptotically justified max-stable or generalized Pareto processes. These models are suitable when asymptotic dependence is present, i.e., the joint tail decays at the same rate as the marginal tail. However, recent environmental data applications suggest that asymptotic independence is equally important and, unfortunately, existing spatial models in this setting that are both flexible and can be fitted efficiently are scarce. Here, we propose a new spatial copula model based on the generalized hyperbolic distribution, which is a specific normal mean-variance mixture and is very popular in financial modeling. The tail properties of this distribution have been studied in the literature, but with contradictory results. It turns out that the proofs from the literature contain mistakes. We here give a corrected theoretical description of its tail dependence structure and then exploit the model to analyze a simulated dataset from the inverted Brown--Resnick process, hindcast significant wave height data in the North Sea, and wind gust data in the state of Oklahoma, USA. We demonstrate that our proposed model is flexible enough to capture the dependence structure not only in the tail but also in the bulk.

How to cite: Zhang, Z., Huser, R., Opitz, T., and Wadsworth, J.: Modeling Spatial Extremes Using Normal Mean-Variance Mixtures, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4136,, 2022.