GIG: Generalized Inverse Gaussian Distribution
In QRM: Provides R-Language Code to Examine Quantitative Risk Management Concepts

Description Usage Arguments Details Value

Calculates (log) moments of univariate generalized inverse Gaussian (GIG) distribution and generating random variates.

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EGIG(lambda, chi, psi, k = 1)
ElogGIG(lambda, chi, psi)
rGIG(n, lambda, chi, psi, envplot = FALSE, messages = FALSE)

`chi`	`numeric`, chi parameter.
`envplot`	`logical`, whether plot of rejection envelope should be created.
`k`	`integer`, order of moments.
`lambda`	`numeric`, lambda parameter.
`messages`	`logical`, whether a message about rejection rate should be returned.
`n`	`integer`, count of random variates.
`psi`	`numeric`, psi parameter.

Normal variance mixtures are frequently obtained by perturbing the variance component of a normal distribution; here this is done by multiplying the square root of a mixing variable assumed to have a GIG distribution depending upon three parameters (lambda, chi, psi). See p.77 in QRM.
Normal mean-variance mixtures are created from normal variance mixtures by applying another perturbation of the same mixing variable to the mean component of a normal distribution. These perturbations create Generalized Hyperbolic Distributions. See pp. 78–81 in QRM. A description of the GIG is given on page 497 in QRM Book.

(log) mean of distribution or vector random variates in case of rgig().

QRM documentation built on April 14, 2020, 6:49 p.m.