  We propose a new class of extreme-value copulas which are extreme-value
limits of conditional normal models. Conditional normal models are
generalizations of conditional independence models, where the dependence among
observed variables is modeled using one unobserved factor. Conditional on this
factor, the distribution of these variables is given by the Gaussian copula.
This structure allows one to build flexible and parsimonious models for data
with complex dependence structures, such as data with spatial dependence or
factor structure. We study the extreme-value limits of these models and show
some interesting special cases of the proposed class of copulas. We develop
estimation methods for the proposed models and conduct a simulation study to
assess the performance of these algorithms. Finally, we apply these copula
models to analyze data on monthly wind maxima and stock return minima.
