qpolicy_loss: Quantile of the policy loss

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

Quantile of the policy loss

Usage

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qpolicy_loss(q, mu, delta, lambda, theta, family, y.max = 20,zt=TRUE)

Arguments

q

value at which the quantile function is evaluated

mu

expectation of the Gamma distribution

delta

dispersion parameter of the Gamma distribution

lambda

parameter of the (zero-truncated) Poisson distribution

theta

copula parameter

family

an integer defining the bivariate copula family: 1 = Gauss, 3 = Clayton, 4=Gumbel, 5=Frank

y.max

upper value of the finite sum that we use to approximate the infinite sum in the density, see below for more details

zt

logical. If zt=TRUE, we use a zero-truncated Poisson variable. Otherwise, we use a Poisson variable. Default is TRUE.

Details

For a Gamma distributed variable X and a (zero truncated) Possion variable Y, the policy loss is defined as L=X\cdot Y. Its density is an infinite sum of weighted Gamma densities. The parameter y.max is the upper value of the finite sum that approximates the infinite sum.

Value

quantile, evaluated at q

Author(s)

Nicole Kraemer

References

N. Kraemer, E. Brechmann, D. Silvestrini, C. Czado (2013): Total loss estimation using copula-based regression models. Insurance: Mathematics and Economics 53 (3), 829 - 839.

See Also

ppolicy_loss,epolicy_loss,dpolicy_loss

Examples

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library(VineCopula)
mu<-1000
delta<-0.09
lambda<-2.5
family<-1
theta<-BiCopTau2Par(tau=0.5,family=family)
# upper quartile
out<-qpolicy_loss(0.75,mu,delta,lambda,theta,family)


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