ghyp.attribution-class: Class ghyp.attribution

In ghyp: Generalized Hyperbolic Distribution and Its Special Cases

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 ghyp.attribution.
...	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)

ghyp documentation built on Sept. 12, 2024, 7:38 a.m.