ghyp: ESghyp.attribution – R documentation

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ESghyp.attribution

Risk attribution.

Description

Functions to get the contribution of each asset to the portfolio's Expected Shortfall based on multivariate generalized hyperbolic distributions as well as the expected shortfall sensitivity to marginal changes in portfolio allocation.

Usage

ESghyp.attribution(
  alpha,
  object = ghyp(),
  distr = c("return", "loss"),
  weights = NULL,
  ...
)

Arguments

`alpha`	a vector of confidence levels for ES.
`object`	a multivariate fitted ghyp object inheriting from class `ghyp`.
`distr`	whether the ghyp-object specifies a return or a loss-distribution (see Details).
`weights`	vector of portfolio weights. Default is an equally-weighted portfolio.
`...`	optional arguments passed from ghyp.attribution to `qghyp` and `integrate`.

Details

The parameter distr specifies whether the ghyp-object describes a return or a loss-distribution. In case of a return distribution the expected-shortfall on a confidence level alpha is defined as ES_alpha := E(X | X <= F^-1(alpha)) while in case of a loss distribution it is defined on a confidence level alpha as ES_alpha := E(X | X > F^-1(alpha)).

Value

ESghyp.attribution is an object of class ghyp.attribution.

Author(s)

Marc Weibel

Examples

## Not run: 
data(smi.stocks)
## Fit a NIG model to Novartis, CS and Nestle log-returns
assets.fit <- fit.NIGmv(smi.stocks[, c("Novartis", "CS", "Nestle")], silent = TRUE)
## Define Weights of the Portfolio
weights <- c(0.2, 0.5, 0.3)
## Confidence level for Expected Shortfall
es.levels <- c(0.01)

portfolio.attrib <- ESghyp.attribution(alpha=es.levels, object=assets.fit, weights=weights)

## End(Not run)

ghyp

Generalized Hyperbolic Distribution and Its Special Cases

v1.6.1

GPL (>= 2)

Authors

Marc Weibel, David Luethi, Wolfgang Breymann

Initial release

2020-04-27