Dependence Modeling with Copulas covers the substantial advances
that have taken place in the field during the last 15 years,
including vine copula modeling of high-dimensional data. Vine
copula models are constructed from a sequence of bivariate copulas.
The book develops generalizations of vine copula models, including
common and structured factor models that extend from the Gaussian
assumption to copulas. It also discusses other multivariate
constructions and parametric copula families that have different
tail properties and presents extensive material on dependence and
tail properties to assist in copula model selection. The author
shows how numerical methods and algorithms for inference and
simulation are important in high-dimensional copula applications.
He presents the algorithms as pseudocode, illustrating their
implementation for high-dimensional copula models. He also
incorporates results to determine dependence and tail properties of
multivariate distributions for future constructions of copula
models.
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