Functions to compute ValueatRisk (VaR), Conditional ValueatRisk (CVaR) and Expected Loss (EL) at data from scaleshape families.
1 2 3 4 5 
data 
data at which to compute the risk measure. 
model 
an object of class 
level 
real: probability needed for VaR and CVaR. 
N0 
real: expected frequency for expected loss. 
rob 
logical; if 
x 
an object of (S3)class 
... 
further arguments for 
The risk measures getVaR
, getCVaR
, getEL
return
an (S3) object of class "riskMeasure"
, i.e., a numeric vector
of length 2 with components "Risk"
and "varofRisk"
containing the respective risk measure
and a corresponding (asymptotic) standard error for the risk
measure. To the return class "riskMeasure"
,
there is a particular print
method; if the corresponding argument
level
is NULL
(default) the corresponding standard error
is printed together with the risk measure; otherwise a corresponding
CLTbased confidence interval for the risk meausre is produced.
Peter Ruckdeschel peter.ruckdeschel@unioldenburg.de
P. Ruckdeschel, N. Horbenko (2013): OptimallyRobust Estimators in Generalized Pareto Models. Statistics 47(4), 762–791. N. Horbenko, P. Ruckdeschel, T. Bae (2011): Robust Estimation of Operational Risk. Journal of Operational Risk 6(2), 3–30.
GParetoFamily
, GEVFamily
, WeibullFamily
, GammaFamily
1 2 3 4 5 6 7 8 9 10 11 12  set.seed(123)
GPD < GParetoFamily(loc=20480, scale=7e4, shape=0.3)
data < r(GPD)(500)
getCVaR(data,GPD,0.99)
## Not run: # to reduce checking time
getVaR(data,GPD,0.99)
getEL(data,GPD,5)
getVaR(data,GPD,0.99, rob=FALSE)
getEL(data,GPD,5, rob=FALSE)
getCVaR(data,GPD,0.99, rob=FALSE)
## End(Not run)

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