R中怎样做全局最小方差组合投资分析？

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想对组合投资进行GMV(global minimum variance)分析，在R中怎样才能实现？主要用到哪个包？谢谢大家解答。

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关键词：投资分析 组合投资 variance minimum varian 投资分析

我给你找了一个例子——



library(xts)
library(quadprog)
library(PerformanceAnalytics)
library(PortfolioAnalytics)
library(pso)


# General Parameters for sample code
data(edhec)
N <- 4
R <- edhec[,1:N]
funds <- names(R)
mu.port <- mean(colMeans(R))


gen.constr <- constraint(assets = funds, min=-2, max=2, min_sum=0.99, max_sum=1.01, risk_aversion=1)
gen.constr <- add.objective(constraints=gen.constr, type="return", name="mean", enabled=FALSE, target=mu.port)
gen.constr <- add.objective(constraints=gen.constr, type="risk", name="var", enabled=FALSE, risk_aversion=10)
gen.constr <- add.objective(constraints=gen.constr, type="risk", name="CVaR", enabled=FALSE)
gen.constr <- add.objective(constraints=gen.constr, type="risk", name="sd", enabled=FALSE)




# =====================
# Max return under box constraints, fully invested
print('Max return under box constraints, fully invested')
max.port <- gen.constr
max.port$min <- rep(0.01,N)
max.port$max <- rep(0.30,N)
max.port$objectives[[1]]$enabled <- TRUE
max.port$objectives[[1]]$target <- NULL
max.port$objectives[[1]]$multiplier  <- -1
max.solution <- optimize.portfolio(R=R, constraints=max.port, optimize_method="pso", trace=TRUE)

# =====================
# Mean-variance:  Fully invested, Global Minimum Variance Portfolio
print('Mean-variance:  Fully invested, Global Minimum Variance Portfolio')
gmv.port <- gen.constr
gmv.port$objectives[[4]]$enabled <- TRUE
gmv.solution <- optimize.portfolio(R=R, constraints=gmv.port, optimize_method="pso", trace=TRUE)

# ========================
# Minimize CVaR
#
print('Min-CVaR')
cvar.port <- gen.constr
cvar.port$min <- rep(0,N)
cvar.port$max <- rep(1,N)
cvar.port$objectives[[3]]$enabled <- TRUE
cvar.port$objectives[[3]]$arguments <- list(p=0.95, clean="boudt")
cvar.solution <- optimize.portfolio(R=R, constraints=cvar.port, optimize_method="pso", trace=TRUE)


要是想了解更详细的内容，这里有个pdf文档，说的就特别清楚了～
http://www.rinfinance.com/agenda/2013/talk/DougMartin.pdf

非常感谢

非常感谢