ScaleEPD {ReIns}

Bias-reduced scale estimator using EPD estimator

Description

Computes the bias-reduced estimator for the scale parameter using the EPD estimator (Beirlant et al., 2016).

Usage

ScaleEPD(data, gamma, kappa, logk = FALSE, plot = FALSE, add = FALSE, 
         main = "Estimates of scale parameter", ...)

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.

logk

Logical indicating if the estimates are plotted as a function of \log(k) (logk=TRUE) or as a function of k. Default is FALSE.

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 of scale parameter".

...

Additional arguments for the plot function, see plot for more details.

Details

The scale estimates are computed based on the following model for the CDF: 1-F(x) = A x^{-1/\gamma} ( 1+ bx^{-\beta}(1+o(1)) ), where A:= C^{1/\gamma} is the scale parameter. Using the EPD approach we replace bx^{-\beta} by \kappa/\gamma.

See Section 4.2.1 of Albrecher et al. (2017) for more details.

Value

A list with following components:

k

Vector of the values of the tail parameter k.

A

Vector of the corresponding scale estimates.

C

Vector of the corresponding estimates for C, see details.

Author(s)

Tom Reynkens

References

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

Beirlant, J., Schoutens, W., De Spiegeleer, J., Reynkens, T. and Herrmann, K. (2016). "Hunting for Black Swans in the European Banking Sector Using Extreme Value Analysis." In: Jan Kallsen and Antonis Papapantoleon (eds.), Advanced Modelling in Mathematical Finance, Springer International Publishing, Switzerland, pp. 147–166.

See Also

Scale, Scale.2o, EPD

Examples

data(secura)

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

# Scale estimator
S <- Scale(secura$size, gamma=H$gamma, plot=FALSE)
# Bias-reduced scale estimator
Sepd <- ScaleEPD(secura$size, gamma=epd$gamma, kappa=epd$kappa, plot=FALSE)

# Plot logarithm of scale             
plot(S$k,log(S$A), xlab="k", ylab="log(Scale)", type="l")
lines(Sepd$k,log(Sepd$A), lty=2)