PAveDD: Portfolio optimisation with average draw down constraint

In FRAPO: Financial Risk Modelling and Portfolio Optimisation with R

Description

This function returns the result of a long-only portfolio optimization whereby the portfolio's (historic) average draw down is constrained to an upper limit.

Usage

PAveDD(PriceData, AveDD = 0.1, softBudget = FALSE, ...)

Arguments

PriceData	A rectangular array of price data.
AveDD	Numeric, the upper bound of the average portfolio draw down.
softBudget	Logical, whether the budget constraint shall be implemented as a soft constraint, i.e. the sum of the weights can be less than one. The default is to use an equality constraint.
...	Arguments are passed down to Rglpk_solve_LP

Details

This function implements a long-only portfolio optimisation with an average draw down constraint (see references below). The problem can be stated in the form of a linear program and GLPK is used as solver.

Value

An object of formal class "PortAdd".

Note

A warning is issued in case the solver had exit status not equal to zero.

Author(s)

Bernhard Pfaff

References

Chekhlov, A. and Uryasev, S. and Zabarankin, M., Portfolio Optimization with Drawdown Constraints, Department of Industrial and Systems Engineering, University of Florida, Research Report 2000-5, 2000, Gainesville, FL. Chekhlov, A. and Uryasev, S. and Zabarankin, M., Drawdown Measure in Portfolio Optimization, International Journal of Theoretical and Applied Finance, 2005, 8(1), 13–58.

See Also

"PortSol", "PortAdd", "PortDD", PMaxDD, PCDaR, PMinCDaR

Examples

## Not run: 
data(StockIndex)
popt <- PAveDD(PriceData = StockIndex, AveDD = 0.1, softBudget = TRUE)

## End(Not run)

Example output

Loading required package: cccp
Loading required package: Rglpk
Loading required package: slam
Using the GLPK callable library version 4.52
Loading required package: timeSeries
Loading required package: timeDate
Financial Risk Modelling and Portfolio Optimisation with R (version 0.4-1)

FRAPO documentation built on May 2, 2019, 6:33 a.m.