Description Usage Arguments Details Author(s) References Examples
Given the object opriskmodel using the single-loss approximation a list of alpha-quantiles is created for every cell in opriskmodel.
1 |
opriskmodel |
Object containing the parameters for the severities and the frequencies |
alpha |
Level of the quantile for the total loss-process of the single-loss-approximation |
xi_low |
Lower interpolation-point for the spline-function, standard value = 0.8 |
xi_high |
Upper interpolation-point for the spline-function, standard value = 1.2 |
plot |
Plot of the interpolated correction term if xi is between xi_low and xi_high, standard value = FALSE |
In the first step the tailindex of the severity distribution is determined. If it does not lie in the critical zone, i.e., not between xi_low and xi_high, the closed-form single-loss approximation from Degen is used. If the tailindex lies in the critical zone, then the R-function splinefun with method "hyman" is used to create a spline with given data points xi lying in [xi_low - 0.2, xi_low] and [xi_high, xi_high + 0.2] using the value at xi = 1 as an anchor. A plot of the interpolation is provided such that user-defined adjustment of xi_low and xi_high is possible.
Benedikt Graswald, Jixuan Wang, Christina Zou
Bocker, Klaus, and Claudia Kluppelberg. "Operational VaR: a closed-form approximation." Risk-London-Rsik Magazine Limited- 18.12 (2005): 90.
Degen, Matthias. "The calculation of minimum regulatory capital using single-loss approximations." The Journal of Operational Risk 5.4 (2010): 3.
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#Example: SLA for the spliced log-gamma gpd model (tail-index = 0.014, no interpolation required)
opriskmodel = list()
opriskmodel[[1]] = list()
opriskmodel[[1]]$sevdist = buildSplicedSevdist("lgamma", c(1.23, 0.012),
"gpd", c(200, 716, 0.014), 2000, 0.8)
opriskmodel[[1]]$freqdist = buildFreqdist("pois", 50)
#Example: SLA for the spliced log-gamma gpd model (tail-index = 0.9, interpolation performed)
opriskmodel[[2]] = list()
opriskmodel[[2]]$sevdist = buildSplicedSevdist("lgamma", c(1.23, 0.012),
"gpd", c(200, 716, 0.9), 2000, 0.8)
opriskmodel[[2]]$freqdist = buildFreqdist("pois", 50)
sla(opriskmodel, alpha = 0.95)
#generate plot if interpolation was performed
sla(opriskmodel, alpha = 0.95, plot = TRUE)
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