ghyp1.6.5 package

Generalized Hyperbolic Distribution and Its Special Cases

portfolio.optimize

Portfolio optimization with respect to alternative risk measures

coef-method

Extract parameters of generalized hyperbolic distribution objects

ESghyp.attribution

Risk attribution.

fit.ghypmv

Fitting generalized hyperbolic distributions to multivariate data

fit.ghypuv

Fitting generalized hyperbolic distributions to univariate data

ghyp-constructors

Create generalized hyperbolic distribution objects

ghyp-distribution

The Generalized Hyperbolic Distribution

ghyp-get

Get methods for objects inheriting from class ghyp

ghyp-internal

Internal ghyp functions

ghyp-mle.ghyp-classes

Classes ghyp and mle.ghyp

ghyp-package

A package on the generalized hyperbolic distribution and its special c...

ghyp-risk-performance

Risk and Performance Measures

ghyp.attribution-class

Class ghyp.attribution

ghyp.moment

Compute moments of generalized hyperbolic distributions

gig-distribution

The Generalized Inverse Gaussian Distribution

hist-methods

Histogram for univariate generalized hyperbolic distributions

lik.ratio.test

Likelihood-ratio test

logLik-AIC-methods

Extract Log-Likelihood and Akaike's Information Criterion

mean-vcov-skew-kurt-methods

Expected value, variance-covariance, skewness and kurtosis of generali...

pairs-methods

Pairs plot for multivariate generalized hyperbolic distributions

plot-ghyp.attribution

Plot ES contribution

plot-lines-methods

Plot univariate generalized hyperbolic densities

qq-ghyp

Quantile-Quantile Plot

scale-methods

Scaling and Centering of ghyp Objects

stepAIC.ghyp

Perform a model selection based on the AIC

summary-method

mle.ghyp summary

transform-extract-methods

Linear transformation and extraction of generalized hyperbolic distrib...

Detailed functionality for working with the univariate and multivariate Generalized Hyperbolic distribution and its special cases (Hyperbolic (hyp), Normal Inverse Gaussian (NIG), Variance Gamma (VG), skewed Student-t and Gaussian distribution). Especially, it contains fitting procedures, an AIC-based model selection routine, and functions for the computation of density, quantile, probability, random variates, expected shortfall and some portfolio optimization and plotting routines as well as the likelihood ratio test. In addition, it contains the Generalized Inverse Gaussian distribution. See Chapter 3 of A. J. McNeil, R. Frey, and P. Embrechts. Quantitative risk management: Concepts, techniques and tools. Princeton University Press, Princeton (2005).

  • Maintainer: Marc Weibel
  • License: GPL (>= 2)
  • Last published: 2024-08-26