portfolio.optimize: Portfolio optimization given a multivariate generalized hyperbolic distribution
Description
This function performs a optimization of a portfolio with respect to one
of the risk measures variance, quantile or expected-shortfall,
a level of risk and the requested portfolio return
given a multivariate generalized hyperbolic distribution.Usage
portfolio.optimize(object, ptf.mean = 0.01,
risk.measure = c("variance", "quantile", "expected-shortfall"),
level = 0.95,...)Arguments
object
A multivariate generalized hyperbolic object.
ptf.mean
The required expected return of the portfolio.
risk.measure
The risk measure to which the portfolio should be optimized.
Must be one of variance, quantile or
expected-shortfall.
level
The level of the risk.measure. Only used when risk.measure
is quantile or expected-shortfall.
...
Arguments passed to optim. Value
- A list with components:
- portfolioAn object of class
ghypuv which
represents the generalized hyperbolic distribution of the portfolio. - risk.measureThe optimization criterion.
- valueThe value of the risk measure.
- opt.weightsThe optimal weights.
- convergenceConvergence returned from
optim. - messageA possible error message returned from
optim. - n.iterThe number of iterations returned from
optim.