ghyp.attribution-class {ghyp} | R Documentation |
Class ghyp.attribution
Description
The class “ghyp.attribution” contains the Expected Shortfall of the portfolio as well as the contribution of each asset to the total risk and the sensitivity of each Asset. The sensitivity gives an information about the overall risk modification of the portfolio if the weight in a given asset is marginally increased or decreased (1 percent).
The function contribution
returns the contribution of the assets to the portfolio expected shortfall.
Usage
contribution(object, ...)
## S4 method for signature 'ghyp.attribution'
contribution(object, percentage = FALSE)
sensitivity(object)
## S4 method for signature 'ghyp.attribution'
sensitivity(object)
## S4 method for signature 'ghyp.attribution'
weights(object)
Arguments
object |
an object inheriting from class |
... |
additional parameters. |
percentage |
boolean. Display figures in percent. (Default=FALSE). |
Details
Expected shortfall enjoys homogeneity, sub-additivity, and co-monotonic additivity. Its associated function is continuously differentiable under moderate assumptions on the joint distribution of the assets.
Value
contribution of each asset to portfolio's overall expected shortfall.
sensitivity of each asset to portfolio's overall expected shortfall.
weights of each asset within portfolio.
Slots
ES
Portfolio's expected shortfall (ES) for a given confidence level. Class
matrix
.contribution
Contribution of each asset to the overall ES. Class
matrix
.sensitivity
Sensitivity of each asset. Class
matrix
.weights
Weight of each asset.
Objects from the Class
Objects should only be created by calls to the constructors ESghyp.attribution
.
Note
When showing special cases of the generalized hyperbolic distribution the corresponding fixed parameters are not printed.
Author(s)
Marc Weibel
Marc Weibel
See Also
ESghyp.attribution
, ghyp.attribution-class
to
compute the expected shortfall attribution.
Examples
## Not run:
data(smi.stocks)
multivariate.fit <- fit.ghypmv(data = smi.stocks,
opt.pars = c(lambda = FALSE, alpha.bar = FALSE),
lambda = 2)
portfolio <- ESghyp.attribution(0.01, multivariate.fit)
summary(portfolio)
## End(Not run)