# ExcessEPD: Estimates for excess-loss premiums using EPD estimates In ReIns: Functions from "Reinsurance: Actuarial and Statistical Aspects"

 ExcessEPD R Documentation

## Estimates for excess-loss premiums using EPD estimates

### Description

Estimate premiums of excess-loss reinsurance with retention R and limit L using EPD estimates.

### Usage

ExcessEPD(data, gamma, kappa, tau, R, L = Inf, warnings = TRUE, plot = TRUE, add = FALSE,
main = "Estimates for premium of excess-loss insurance", ...)

### Arguments

 data Vector of n observations. gamma Vector of n-1 estimates for the EVI, obtained from EPD. kappa Vector of n-1 estimates for \kappa, obtained from EPD. tau Vector of n-1 estimates for \tau, obtained from EPD. R The retention level of the (re-)insurance. L The limit of the (re-)insurance, default is Inf. warnings Logical indicating if warnings are displayed, default is TRUE. plot Logical indicating if the estimates should be plotted as a function of k, default is FALSE. add Logical indicating if the estimates should be added to an existing plot, default is FALSE. main Title for the plot, default is "Estimates for premium of excess-loss insurance". ... Additional arguments for the plot function, see plot for more details.

### 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.

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 (X_{n-k,n})^{1/\hat{\gamma}} \times ((1-\hat{\kappa}/\hat{\gamma})(1/\hat{\gamma}-1)^{-1}u^{1-1/\hat{\gamma}} + \hat{\kappa}/(\hat{\gamma}X_{n-k,n}^{\hat{\tau}})(1/\hat{\gamma}-\hat{\tau}-1)^{-1}u^{1+\hat{\tau}-1/\hat{\gamma}})

with \hat{\gamma}, \hat{\kappa} and \hat{\tau} the estimates for the parameters of the EPD.

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 k. premium The corresponding estimates for the premium. R The retention level of the (re-)insurance. L The limit of the (re-)insurance.

Tom Reynkens

### References

Albrecher, H., Beirlant, J. and Teugels, J. (2017). Reinsurance: Actuarial and Statistical Aspects, Wiley, Chichester.

epd <- EPD(secura$size) # Premium of excess-loss insurance with retention R R <- 10^7 ExcessEPD(secura$size, gamma=epd$gamma, kappa=epd$kappa, tau=epd\$tau, R=R, ylim=c(0,2*10^4))