ghyp (version 1.1.0)

fit.ghypuv: Fitting generalized hyperbolic distributions to univariate data

Description

This function performs a maximum likelihood parameter estimation for univariate generalized hyperbolic distributions.

Usage

fit.ghypuv(data, lambda = 1, alpha.bar = 0.1, mu = mean(data), 
           sigma = sd(data), gamma = 0, 
           opt.pars = c(lambda = T, alpha.bar = T, mu = T, 
                        sigma = T, gamma = !symmetric), 
           symmetric = F, standardize = F, save.data = T, 
           na.rm = T, silent = FALSE, ...)
           
fit.hypuv(data, 
          opt.pars = c(alpha.bar = T, mu = T, sigma = T, gamma = !symmetric), 
          symmetric = F, ...)
fit.NIGuv(data, 
          opt.pars = c(alpha.bar = T, mu = T, sigma = T, gamma = !symmetric), 
          symmetric = F, ...)
fit.VGuv(data, lambda = 1, 
         opt.pars = c(lambda = T, mu = T, sigma = T, gamma = !symmetric), 
         symmetric = F, ...)
fit.tuv(data, nu = 3.5, 
        opt.pars = c(nu = T, mu = T, sigma = T, gamma = !symmetric), 
        symmetric = F, ...)

Arguments

data

An object coercible to a vector.

lambda

Shape parameter.

alpha.bar

Shape parameter.

nu

Shape parameter only used in case of a student-t distribution. It determines the degree of freedom and is defined as -2*lambda.

mu

Location parameter.

sigma

Dispersion parameter.

gamma

Skewness parameter.

opt.pars

A named logical vector which states which parameters should be fitted.

symmetric

If TRUE the skewness parameter gamma keeps zero.

standardize

If TRUE the sample will be standardized before fitting. Afterwards, the parameters and log-likelihood et cetera will be back-transformed.

save.data

If TRUE data will be stored within the mle.ghyp object.

na.rm

If TRUE missing values will be removed from data.

silent

If TRUE no prompts will appear in the console.

...

Arguments passed to optim and to fit.ghypuv when fitting special cases of the generalized hyperbolic distribution.

Value

• An object of class mle.ghyp.

Details

The general-purpose optimization routine optim is used to maximize the loglikelihood function. The default method is that of Nelder and Mead which uses only function values. Parameters of optim can be passed via the ...argument of the fitting routines.

See Also

fit.ghypmv, fit.hypmv, fit.NIGmv, fit.VGmv, fit.tmv for multivariate fitting routines.

Examples

data(smi.stocks)
  fit.NIGuv(data = smi.stocks[,"SMI"], opt.pars = c(alpha.bar = FALSE),
            alpha.bar = 1, control = list(abs.tol = 1e-5, maxit = 100))