portfolio.optimize {ghyp}R Documentation

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:

portfolio An object of class ghypuv which represents the generalized hyperbolic distribution of the portfolio.
risk.measure The optimization criterion.
value The value of the risk measure.
opt.weights The optimal weights.
convergence Convergence returned from optim.
message A possible error message returned from optim.
n.iter The number of iterations returned from optim.

Note

There is no constraint implemented yet determining whether short selling is allowed or not.

If the risk measure is “variance” the returned portfolio is simply an efficient frontier.

Author(s)

David Lüthi

See Also

lin.transf, fit.ghypmv

Examples



[Package ghyp version 0.9.2 Index]