Estimation of the Hyperbolic Distribution

Performs a maximum likelihood estimation on the Hyperbolic distribution (univariate and multivariate). Wrappers fit.hypuv and fit.hypmv

fit_hyp(x, symmetric = FALSE)

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

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

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

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

Arguments

x	A tabular (non-tidy) data structure.
symmetric	A 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_hyp(x)
#> # Margins Estimation
#> Converged:       TRUE
#> Dimension:       4
#> AIC:            -52714.94
#> Log-Likelihood:  26376.47
#> Model:           Asymmetric Hyperbolic

# univariate estimation
fit_hyp(x[ , 4, drop = FALSE])
#> # Margins Estimation
#> Converged:       TRUE
#> Dimension:       1
#> AIC:            -12784.62
#> Log-Likelihood:  6396.311
#> Model:           Asymmetric Hyperbolic