copula_transformation: Copula Normalization

In Reckziegel/CMA: Copula Marginal Algorithm (CMA)

Copula Normalization

Description

Transforms the entries an arbitrary copula so they can be compatible with the first two moments of a timeseries.

Usage

copula_transformation(x, copula)

Arguments

x	A multivariate (non-tidy) dataset.
copula	A copula.

Details

Under the hood, the copula transformation is a 4-step recipe:

• 1. Normalize the copula to have a standard normal distribution;

• 2. Operate an affine transformation on the normalized variables;

• 3. Do the CMA Separation step on the new rotated variables;

• 4. Extract the copula component that results from step 3.

Value

A tabular (non-tidy) tibble.

Examples

x <- matrix(diff(log(EuStockMarkets)), ncol = 4)

sep     <- cma_separation(x)
clayton <- fit_copula_clayton(sep)
gen     <- generate_copulas(clayton, 10)

copula_transformation(x, gen)

Reckziegel/CMA documentation built on July 13, 2022, 10:31 p.m.