random.benchmark.test: Random Naive Benchmark Portfolio

Description

This function generates a vector of investment weights for a benchmark portfolio where the weights are non-negative and the sume of the weights is a given total. 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

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

Arguments

A positive integer for the number of investments in the portfolio

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 details are described in the function random.benchmark.

Examples

Run this code

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