pmSolver-methods: Methods: Partial Moment Quadratic Portfolio Solver

Description Usage Arguments Value Examples

Description

Implements the quadratic portfolio optimization algorithm using the partial co-moments symmetric expectations matrix

Usage

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pmSolver(pcmExpectation, userConstraints, npoints=200, type=c("I","II","III","IV"))

Arguments

pcmExpectation

The symmetric co-partial moments. An PCME object created by calling the pcmExpectation method.

userConstraints

The portfolio constraints object (userConstraints) created by calling the constraints method.

npoints

(Optional) No of frontier points.

type

Optimization type required. Valid methods are “I” for the Minimum Risk Portfolio, “II” for the Minimizing risk for a given level of return, “III” for maximizing the risk adjusted return for a given risk aversion coefficient, and “IV” for the efficient frontier.

Value

An object of class PMSOLVER.

Examples

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## Not run: 
data(djiaret)
# calculate sample lower partial moments by bootstrap
# REM: for optimization you should use standardized moments 
# (standardize=TRUE)
sample.lpm<-pmExpectation(data=as.matrix(djiaret), method=rep("boot",30), 
			threshold=-0.01, moment=2, type="lower", standardize=TRUE)
# calculate sample lower partial co-moment matrix
sample.spcm<-pcmExpectation(sample.lpm, corr=cor(as.matrix(djiaret)), tail="lower")
##############################################
# Lower Partial Moments Portfolio Optimization
##############################################
# Test13: Constraints Setup
cst<-constraints(forecasts=apply(as.matrix(djiaret),2,FUN=function(x) mean(x)), 
		portfolioReturn=0.1/252, riskAversion = 2, riskFree=0.05/252, 
		budget = 1, assetsUB = rep(0.25,30),assetsLB=rep(0,30))

# Test14: Minimum Risk Solver (I)
port3<-pmSolver(sample.spcm, cst, type="I")
show(port3)
weightsPie(port3)

# Test15: Minimize Risk for given level of Return (II)
# REM portfolioReturn=0.1/252 (in constraints)
port4<-pmSolver(sample.spcm, cst, type="II")
show(port4)
weightsPie(port4)

# Test16: Maximize the risk adjusted return for a given risk aversion 
# coefficient (III)
getriskAversion(cst)
setriskAversion(cst) = 0.1
port5<-pmSolver(sample.spcm, cst, type="III")
show(port5)
weightsPie(port5)

getriskAversion(cst)
setriskAversion(cst) = 40
port6<-pmSolver(sample.spcm, cst, type="III")
show(port6)
weightsPie(port6)

# Test17: Efficient Frontier (IV)
port7<-pmSolver(sample.spcm, cst, npoints=200, type="IV")

plot(port7)
dev.new(2)
weightsPlot(port7)

## End(Not run)

pmoments documentation built on May 31, 2017, 1:48 a.m.