ExcessGPD {ReIns} | R Documentation |
Estimates for excess-loss premiums using GPD-MLE estimates
Description
Estimate premiums of excess-loss reinsurance with retention R
and limit L
using GPD-MLE estimates.
Usage
ExcessGPD(data, gamma, sigma, R, L = Inf, warnings = TRUE, plot = TRUE, add = FALSE,
main = "Estimates for premium of excess-loss insurance", ...)
Arguments
data |
Vector of |
gamma |
Vector of |
sigma |
Vector of |
R |
The retention level of the (re-)insurance. |
L |
The limit of the (re-)insurance, default is |
warnings |
Logical indicating if warnings are displayed, default is |
plot |
Logical indicating if the estimates should be plotted as a function of |
add |
Logical indicating if the estimates should be added to an existing plot, default is |
main |
Title for the plot, default is |
... |
Additional arguments for the |
Details
We need that u \ge X_{n-k,n}
, the (k+1)
-th largest observation.
If this is not the case, we return NA
for the premium. A warning will be issued in
that case if warnings=TRUE
. One should then use global fits: ExcessSplice
.
The premium for the excess-loss insurance with retention R
and limit L
is given by
E(\min{(X-R)_+, L}) = \Pi(R) - \Pi(R+L)
where \Pi(u)=E((X-u)_+)=\int_u^{\infty} (1-F(z)) dz
is the premium of the excess-loss insurance with retention u
. When L=\infty
, the premium is equal to \Pi(R)
.
We estimate \Pi
by
\hat{\Pi}(u) = (k+1)/(n+1) \times \hat{\sigma}_k/ (1-\hat{\gamma}_k) \times (1+\hat{\gamma}_k/\hat{\sigma}_k (u-X_{n-k,n}))^{1-1/\hat{\gamma}_k},
with \hat{\gamma}_k
and \hat{\sigma}_k
the estimates for the parameters of the GPD.
See Section 4.6 of Albrecher et al. (2017) for more details.
Value
A list with following components:
k |
Vector of the values of the tail parameter |
premium |
The corresponding estimates for the premium. |
R |
The retention level of the (re-)insurance. |
L |
The limit of the (re-)insurance. |
Author(s)
Tom Reynkens
References
Albrecher, H., Beirlant, J. and Teugels, J. (2017). Reinsurance: Actuarial and Statistical Aspects, Wiley, Chichester.
See Also
Examples
data(secura)
# GPDmle estimator
mle <- GPDmle(secura$size)
# Premium of excess-loss insurance with retention R
R <- 10^7
ExcessGPD(secura$size, gamma=mle$gamma, sigma=mle$sigma, R=R, ylim=c(0,2*10^4))