  Colored graphical models provide a parsimonious approach to modeling
high-dimensional data by exploiting symmetries in the model parameters. In this
work, we introduce the notion of coloring for extremal graphical models on
multivariate Pareto distributions, a natural class of limiting distributions
for threshold exceedances. Thanks to a stability property of the multivariate
Pareto distributions, colored extremal tree models can be defined fully
nonparametrically. For more general graphs, the parametric family of
H\"usler--Reiss distributions allows for two alternative approaches to colored
graphical models. We study both model classes and introduce statistical
methodology for parameter estimation. It turns out that for H\"usler--Reiss
tree models the different definitions of colored graphical models coincide. In
addition, we show a general parametric description of extremal conditional
independence statements for H\"usler--Reiss distributions. Finally, we
demonstrate that our methodology outperforms existing approaches on a real data
set.
