1 research outputs found
Growing simplified vine copula trees: improving Di{\ss}mann's algorithm
Vine copulas are pair-copula constructions enabling multivariate dependence
modeling in terms of bivariate building blocks. One of the main tasks of
fitting a vine copula is the selection of a suitable tree structure. For this
the prevalent method is a heuristic called Di{\ss}mann's algorithm. It
sequentially constructs the vine's trees by maximizing dependence at each tree
level, where dependence is measured in terms of absolute Kendall's .
However, the algorithm disregards any implications of the tree structure on the
simplifying assumption that is usually made for vine copulas to keep inference
tractable. We develop two new algorithms that select tree structures focused on
producing simplified vine copulas for which the simplifying assumption is
violated as little as possible. For this we make use of a recently developed
statistical test of the simplifying assumption. In a simulation study we show
that our proposed methods outperform the benchmark given by Di{\ss}mann's
algorithm by a great margin. Several real data applications emphasize their
practical relevance.Comment: 24 page