clg function

Compound Laplace gamma distribution

Compound Laplace gamma distribution

Computes the pdf, cdf, value at risk and expected shortfall for the compound Laplace gamma distribution given by [REMOVE_ME] \begin{array}{ll}&\displaystylef (x) = \frac {a b}{2} \left\{ 1 + b \left | x - \theta \right | \right\}^{-\left( a + 1 \right)},\\&\displaystyleF (x) =\left\{\begin{array}{ll}\displaystyle\frac {1}{2} \left\{ 1 + b \left | x - \theta \right | \right\}^{-a}, &\mbox{if $x \leq \theta$,}\\\\\displaystyle1 - \frac {1}{2} \left\{ 1 + b \left | x - \theta \right | \right\}^{-a}, &\mbox{if $x > \theta$,}\end{array}\right.\\&\displaystyle{\rm VaR}_p (X) =\left\{\begin{array}{ll}\displaystyle\theta - \frac {1}{b} - \frac {(2 p)^{-1/a}}{b}, & \mbox{if $p \leq 1/2$,}\\\\\displaystyle\theta - \frac {1}{b} + \frac {(2 (1 - p))^{-1/a}}{b}, &\mbox{if $p > 1/2$,}\end{array}\right.\\&\displaystyle{\rm ES}_p (X) = \left\{\begin{array}{ll}\displaystyle\theta - \frac {1}{b} - \frac {(2 p)^{-1/a}}{b (1 - 1/a)}, &\mbox{if $p \leq 1/2$,}\\\\\displaystyle\theta - \frac {1}{b} - \frac {\left[ 2 (1 - p) \right]^{1 - 1/a}}{2 p b (1 - 1/a)}, &\mbox{if $p > 1/2$}\end{array}\right.\end{array} [REMOVE_ME_2]

for <x<-\infty < x < \infty, 0<p<10 < p < 1, <θ<-\infty < \theta < \infty, the location parameter, b>0b > 0, the scale parameter, and a>0a > 0, the shape parameter.

Description

Computes the pdf, cdf, value at risk and expected shortfall for the compound Laplace gamma distribution given by

\begin{array}{ll}&\displaystylef (x) = \frac {a b}{2} \left\{ 1 + b \left | x - \theta \right | \right\}^{-\left( a + 1 \right)},\\&\displaystyleF (x) =\left\{\begin{array}{ll}\displaystyle\frac {1}{2} \left\{ 1 + b \left | x - \theta \right | \right\}^{-a}, &\mbox{if $x \leq \theta$,}\\\\\displaystyle1 - \frac {1}{2} \left\{ 1 + b \left | x - \theta \right | \right\}^{-a}, &\mbox{if $x > \theta$,}\end{array}\right.\\&\displaystyle{\rm VaR}_p (X) =\left\{\begin{array}{ll}\displaystyle\theta - \frac {1}{b} - \frac {(2 p)^{-1/a}}{b}, & \mbox{if $p \leq 1/2$,}\\\\\displaystyle\theta - \frac {1}{b} + \frac {(2 (1 - p))^{-1/a}}{b}, &\mbox{if $p > 1/2$,}\end{array}\right.\\&\displaystyle{\rm ES}_p (X) = \left\{\begin{array}{ll}\displaystyle\theta - \frac {1}{b} - \frac {(2 p)^{-1/a}}{b (1 - 1/a)}, &\mbox{if $p \leq 1/2$,}\\\\\displaystyle\theta - \frac {1}{b} - \frac {\left[ 2 (1 - p) \right]^{1 - 1/a}}{2 p b (1 - 1/a)}, &\mbox{if $p > 1/2$}\end{array}\right.\end{array}

for <x<-\infty < x < \infty, 0<p<10 < p < 1, <θ<-\infty < \theta < \infty, the location parameter, b>0b > 0, the scale parameter, and a>0a > 0, the shape parameter.

dclg(x, a=1, b=1, theta=0, log=FALSE) pclg(x, a=1, b=1, theta=0, log.p=FALSE, lower.tail=TRUE) varclg(p, a=1, b=1, theta=0, log.p=FALSE, lower.tail=TRUE) esclg(p, a=1, b=1, theta=0)

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
  • theta: the value of the location parameter, can take any real value, the default is zero
  • 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

Returns

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

Stephen Chan, Saralees Nadarajah & Emmanuel Afuecheta (2016). An R Package for Value at Risk and Expected Shortfall, Communications in Statistics - Simulation and Computation, 45:9, 3416-3434, tools:::Rd_expr_doi("10.1080/03610918.2014.944658")

Author(s)

Saralees Nadarajah

Examples

x=runif(10,min=0,max=1) dclg(x) pclg(x) varclg(x) esclg(x)
  • Maintainer: Leo Belzile
  • License: GPL (>= 2)
  • Last published: 2023-04-22

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