ExcessPareto: Estimates for excess-loss premiums using a Pareto model

In ReIns: Functions from "Reinsurance: Actuarial and Statistical Aspects"

Estimates for excess-loss premiums using a Pareto model

Description

Estimate premiums of excess-loss reinsurance with retention R and limit L using a (truncated) Pareto model.

Usage

ExcessPareto(data, gamma, R, L = Inf, endpoint = Inf, warnings = TRUE, plot = TRUE, 
        add = FALSE, main = "Estimates for premium of excess-loss insurance", ...)
        
ExcessHill(data, gamma, R, L = Inf, endpoint = 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 Hill or trHill.
R	The retention level of the (re-)insurance.
L	The limit of the (re-)insurance, default is Inf.
endpoint	Endpoint for the truncated Pareto distribution. When Inf, the default, the ordinary Pareto model is used.
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. 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 (for the untruncated Pareto distribution) by

\hat{\Pi}(u) = (k+1)/(n+1) / (1/H_{k,n}-1) \times (X_{n-k,n}^{1/H_{k,n}} u^{1-1/H_{k,n}}),

with H_{k,n} the Hill estimator.

The ExcessHill function is the same function but with a different name for compatibility with old versions of the package.

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.

Author(s)

Tom Reynkens

References

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

See Also

Hill, ExcessEPD, ExcessGPD, ExcessSplice

Examples

data(secura)

# Hill estimator
H <- Hill(secura$size)

# Premium of excess-loss insurance with retention R
R <- 10^7
ExcessPareto(secura$size, H$gamma, R=R)

ReIns documentation built on Nov. 3, 2023, 5:08 p.m.