rportfolios (version 1.0-1)

rbenchmark.test: Generate random naive benchmark portfolios

Description

This function generates m random long only benchmark portfolios with n investments where the sume of the weights equals a given amount. The weights are naively derived from an i.i.d. sample of truncated random variables. This function is used to evaluate the performance of the portfolio generation algorithm.

Usage

rbenchmark.test(m, n = 2, k = n, segments = NULL, x.t = 1, 
margins = c("unif", "beta", "exp", "frechet", 
"gamma", "gev", "gpd", "gumbel", "lnorm", "logis", "norm", 
"weibull"), ...)

Arguments

m

A positive integer value for the number of portfolios

n

A positive integer for the number of investments in the portfolio

k

A positive integer for the number of non-zero exposures or cardinality

segments

A vector or list of vectors that defines the investment segments

x.t

A positive real value for the sum of the investment exposures

margins

A character value for the underlying distribution of the truncated random variable. The default is a uniform distribution

...

Other arguments passed to the random variate simulation function

Value

A list with two named components.

Details

The function executes the function random.benchmark.test using the R function lapply. The result which is a list contains the investment weight vectors and number of iterations. Thse data are stored in a matrix of investment weights and a vector of iterations. These arrays are returned as a list.

See Also

random.benchmark.test

Examples

###
### 100 long only portfolios of 30 investments with 30 non-zero positions
### the margins of the truncated random variables are uniform
###
p.1.result <- rbenchmark.test( 100, 30 )
###
### 100 long only portfolios of 30 investments with 10 non-zero positions
### the margins of the truncated random variables are uniform
###
p.2.result <- rbenchmark.test( 100, 30, 10 )
###
### 100 long only portfolios of 30 investments with 30 non-zero positions
### the margins of the truncated random variables are log normal
### with zero log mean and unit log standard deviation
###
p.3.result <- rbenchmark.test( 100, 30, margins="lnorm", meanlog=0, sdlog=1 )
###
### 100 long only portfolios of 30 investments with 10 non-zero positions
### the margins of the truncated random variables are log norm
### with zero log mean and unit log standard deviation
###
p.4.result <- rbenchmark.test( 100, 30, 10, margins="lnorm", meanlog=0, sdlog=1 )