getCVaR {RobExtremes} | R Documentation |
Functions to compute Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR) and Expected Loss (EL) at data from scale-shape families.
getVaR(data, model, level, rob=TRUE) getCVaR(data, model, level, rob=TRUE) getEL(data, model, N0, rob=TRUE) ## S3 method for class 'riskMeasure' print(x, level=NULL, ...)
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
CLT-based confidence interval for the risk meausre is produced.
Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de
P. Ruckdeschel, N. Horbenko (2013): Optimally-Robust 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
# to reduce checking time set.seed(123) GPD <- GParetoFamily(loc=20480, scale=7e4, shape=0.3) data <- r(GPD)(500) getCVaR(data,GPD,0.99) 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)