Generalised hyperbolic distribution

Generalised hyperbolic distribution

Probability distribution
name =generalised hyperbolic
type =density
pdf_

cdf_

parameters =mu location (real) lambda (real) alpha (real) eta asymmetry parameter (real) delta scale parameter (real) gamma = sqrt{alpha^2 - eta^2}
support =x in (-infty; +infty)!
pdf =frac{(gamma/delta)^lambda}{sqrt{2pi}K_lambda(delta gamma)} ; e^{eta (x - mu)} ! imes frac{K_{lambda - 1/2}left(alpha sqrt{delta^2 + (x - mu)^2} ight)}{left(sqrt{delta^2 + (x - mu)^2} / alpha ight)^{1/2 - lambda
!
cdf =
mean =mu + frac{delta eta K_{lambda+1}(delta gamma)}{gamma K_lambda(deltagamma)}
median =
mode =
variance =frac{delta K_{lambda+1}(delta gamma)}{gamma K_lambda(deltagamma)} + frac{eta^2delta^2}{gamma^2}left( frac{K_{lambda+2}(deltagamma)}{K_{lambda}(deltagamma)} - frac{K_{lambda+1}^2(deltagamma)}{K_{lambda}^2(deltagamma)} ight)
skewness =
kurtosis =
entropy =
mgf =frac{e^{mu z}gamma^lambda}{(sqrt{alpha^2 -(eta +z)^2})^lambda} frac{K_lambda(delta sqrt{alpha^2 -(eta +z)^2})}{K_lambda (delta gamma)}
char =

The generalised hyperbolic distribution (GH) is a continuous probability distribution defined as the normal variance-mean mixture where the mixing distribution is the generalized inverse Gaussian distribution. Its probability density function (see the box) is given in terms of modified Bessel function of the third kind, denoted by K_lambda.

As the name suggests it is of a very general form, being the superclass of, among others, the Student's "t"-distribution, the Laplace distribution, the hyperbolic distribution, the normal-inverse Gaussian distribution and the variance-gamma distribution.

Its main areas of application are those which require sufficient probability of far-field behaviour, which it can model due to its semi-heavy tails, a property that the normal distribution does not possess. The generalised hyperbolic distribution is well-used in economics, with particular application in the fields of modelling financial markets and risk management, due to its semi-heavy tails. This class is closed under linear operations. It was introduced by Ole Barndorff-Nielsen.

Related distributions

* X sim mathrm{GH}(-frac{ u}{2}, 0, 0, sqrt{ u}, mu), has a Student's "t"-distribution with u degrees of freedom.
* X sim mathrm{GH}(1, alpha, eta, delta, mu), has a hyperbolic distribution.

* X sim mathrm{GH}(-1/2, alpha, eta, delta, mu), has a normal-inverse Gaussian distribution (NIG).
* X sim mathrm{GH}(?, ?, ?, ?, ?), normal-inverse chi-square distribution
* X sim mathrm{GH}(?, ?, ?, ?, ?), normal-inverse gamma distribution (NI)

* X sim mathrm{GH}(lambda, alpha, eta, 0, mu), has a variance-gamma distribution.


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