fit_ghd: Estimation of the Generalized Hyperbolic Distribution

In Reckziegel/CMA: Copula Marginal Algorithm (CMA)

Estimation of the Generalized Hyperbolic Distribution

Description

Performs the estimation on the Generalized Hyperbolic distribution (univariate and multivariate). Wrappers fit.ghypuv and fit.ghypmv.

Usage

fit_ghd(x, symmetric = FALSE)

## Default S3 method:
fit_ghd(x, symmetric = FALSE)

## S3 method for class 'tbl'
fit_ghd(x, symmetric = FALSE)

## S3 method for class 'xts'
fit_ghd(x, symmetric = FALSE)

## S3 method for class 'matrix'
fit_ghd(x, symmetric = FALSE)

Arguments

x	A tabular (non-tidy) data structure.
symmetric	A logical flag. Should the fitted distribution be symmetric? Defaults to FALSE.

Value

A list of the the class cma_fit with 21 components.

See Also

fit_hyp fit_nig fit_vg fit_t fit_normal

Examples

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

# multivariate estimation
fit_ghd(x)

# univariate estimation
fit_ghd(x[ , 3, drop = FALSE])

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