Estimation of the Generalized Hyperbolic Distribution

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

fit_ghd(x, symmetric = FALSE)

# S3 method for default
fit_ghd(x, symmetric = FALSE)

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

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

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

Arguments

x	A tabular (non-tidy) data structure.
symmetric	A logical flag. Should the estimated 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)
#> # Margins Estimation
#> Converged:       TRUE
#> Dimension:       4
#> AIC:            -56122.36
#> Log-Likelihood:  28081.18
#> Model:           Asymmetric Generalized Hyperbolic

# univariate estimation
fit_ghd(x[ , 3, drop = FALSE])
#> # Margins Estimation
#> Converged:       TRUE
#> Dimension:       1
#> AIC:            -11563.82
#> Log-Likelihood:  5786.912
#> Model:           Asymmetric Generalized Hyperbolic