VaRES (version 1.0)

Gamma: Gamma distribution

Description

Computes the pdf, cdf, value at risk and expected shortfall for the gamma distribution given by $$\begin{array}{ll} &\displaystyle f (x) = \frac {b^a x^{a - 1} \exp (-b x)}{\Gamma (a)}, \\ &\displaystyle F (x) = \frac {\gamma (a, b x)}{\Gamma (a)}, \\ &\displaystyle {\rm VaR}_p (X) = \frac {1}{b} Q^{-1} (a, 1 - p), \\ &\displaystyle {\rm ES}_p (X) = \frac {1}{b p} \int_0^p Q^{-1} (a, 1 - v) dv \end{array}$$ for \(x > 0\), \(0 < p < 1\), \(b > 0\), the scale parameter, and \(a > 0\), the shape parameter, where \(\gamma (a, x) = \int_0^x t^{a - 1} \exp \left( -t \right) dt\) denotes the incomplete gamma function, \(Q (a, x) = \int_x^\infty t^{a - 1} \exp \left( -t \right) dt / \Gamma (a)\) denotes the regularized complementary incomplete gamma function, \(\Gamma (a) = \int_0^\infty t^{a - 1} \exp \left( -t \right) dt\) denotes the gamma function, and \(Q^{-1} (a, x)\) denotes the inverse of \(Q (a, x)\).

Usage

dGamma(x, a=1, b=1, log=FALSE)
pGamma(x, a=1, b=1, log.p=FALSE, lower.tail=TRUE)
varGamma(p, a=1, b=1, log.p=FALSE, lower.tail=TRUE)
esGamma(p, a=1, b=1)

Arguments

x

scaler or vector of values at which the pdf or cdf needs to be computed

p

scaler or vector of values at which the value at risk or expected shortfall needs to be computed

b

the value of the scale parameter, must be positive, the default is 1

a

the value of the shape parameter, must be positive, the default is 1

log

if TRUE then log(pdf) are returned

log.p

if TRUE then log(cdf) are returned and quantiles are computed for exp(p)

lower.tail

if FALSE then 1-cdf are returned and quantiles are computed for 1-p

Value

An object of the same length as x, giving the pdf or cdf values computed at x or an object of the same length as p, giving the values at risk or expected shortfall computed at p.

References

S. Nadarajah, S. Chan and E. Afuecheta, An R Package for value at risk and expected shortfall, submitted

Examples

# NOT RUN {
x=runif(10,min=0,max=1)
dGamma(x)
pGamma(x)
varGamma(x)
esGamma(x)
# }