What is R vine copula?

Vine copulas are a flexible class of dependence models consisting of bivariate building blocks (see e.g., Aas et al., 2009). You can find a comprehensive list of publications and other materials on vine-copula.org. This package is primarily made for the statistical analysis of vine copula models.

Why use vine copula?

Therefore, vine copula is invented exactly to address this high dimensional probabilistic modeling problem. Instead of using an N-dimensional copula directly, it decomposes the probability density into conditional probabilities, and further decomposes conditional probabilities into bivariate copulas.

What is a copula model?

In probability theory and statistics, a copula is a multivariate cumulative distribution function for which the marginal probability distribution of each variable is uniform on the interval [0, 1]. Copulas are used to describe/model the dependence (inter-correlation) between random variables.

How do copulas work?

Copulas allow us to decompose a joint probability distribution into their marginals (which by definition have no correlation) and a function which couples (hence the name) them together and thus allows us to specify the correlation seperately. The copula is that coupling function.

How many types of copula are there?

There are three broad classifications of copulas available in @RISK.

How do you calculate a copula?

The simplest copula is the uniform density for independent draws, i.e., c(u,v) = 1, C(u,v) = uv. Two other simple copulas are M(u,v) = min(u,v) and W(u,v) = (u+v–1)+, where the “+” means “zero if negative.” A standard result, given for instance by Wang[8], is that for any copula 3 Page 4 C, W(u,v) ≤ C(u,v) ≤ M(u,v).

How do you find the copula?

What is a normal copula?

Normal Copula. The resultant pattern of a scatter plot of data that helps to provide insight into the correlation (relationships) between different variables in a bi-variate or multi-variate matrix analysis. That is, the intersection of two or more probability distributions or other types of distributions.