rbenchmark.test: Generate random naive benchmark portfolios
In rportfolios: Random Portfolio Generation
Description
Usage
Arguments
Details
Value
Author(s)
See Also
Examples
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
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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
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.
Value
A list with two named components.
xmatrix
A numerical m \times n matrix of investment weights
iters
An integer m \times 1 vector for the number iterations used
to obtain the investment weights
Author(s)
Frederick Novomestky fn334@nyu.edu
See Also
Examples
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###
### 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 )
rportfolios documentation built on May 2, 2019, 3:40 p.m.
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Description Usage Arguments Details Value Author(s) See Also Examples
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
1 2 3 4 | 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 |
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.
Value
A list with two named components.
xmatrix |
A numerical m \times n matrix of investment weights |
iters |
An integer m \times 1 vector for the number iterations used to obtain the investment weights |
Author(s)
Frederick Novomestky fn334@nyu.edu
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ###
### 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 )
|
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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