VaRratio.SE | R Documentation |
VaRratio.SE
computes the standard error of the value-at-risk ratio of the returns.
VaRratio.SE( data, alpha = 0.1, rf = 0, se.method = c("IFiid", "IFcor", "IFcorAdapt", "IFcorPW", "BOOTiid", "BOOTcor")[c(1, 4)], cleanOutliers = FALSE, fitting.method = c("Exponential", "Gamma")[1], d.GLM.EN = 5, freq.include = c("All", "Decimate", "Truncate")[1], freq.par = 0.5, corOut = c("none", "retCor", "retIFCor", "retIFCorPW")[1], return.coef = FALSE, ... )
data |
Data of returns for one or multiple assets or portfolios. |
alpha |
The tail probability of interest. |
rf |
Risk-free interest rate. |
se.method |
A character string indicating which method should be used to compute
the standard error of the estimated standard deviation. One or a combination of:
|
cleanOutliers |
Boolean variable to indicate whether the pre-whitenning of the influence functions TS should be done through a robust filter. Default if FALSE. |
fitting.method |
Distribution used in the standard errors computation. Should be one of "Exponential" (default) or "Gamma". |
d.GLM.EN |
Order of the polynomial for the Exponential or Gamma fitting. Default polynomial order of 5. |
freq.include |
Frequency domain inclusion criteria. Must be one of "All" (default), "Decimate" or "Truncate." |
freq.par |
Percentage of the frequency used if |
corOut |
Return correlation of the returns or the influence function transformed returns. Must be one of "retCor", "retIFCor" or "none" (default). |
return.coef |
Boolean variable to indicate whether the coefficients of the penalized GLM fit are returned. Default if FALSE. |
... |
Additional parameters. |
A vector or a list depending on se.method
.
Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca
# Loading data data(edhec, package = "PerformanceAnalytics") # Changing the data colnames names(edhec) = c("CA", "CTA", "DIS", "EM", "EMN", "ED", "FIA", "GM", "LS", "MA", "RV", "SS", "FOF") # Computing the standard errors for # the two influence functions based approaches VaRratio.SE(edhec, se.method = c("IFiid","IFcorAdapt"), cleanOutliers = FALSE, fitting.method = c("Exponential", "Gamma")[1])
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