assetsSim {fAssets}R Documentation

Simulating Multivariate Asset Sets

Description

Simulates multivariate artificial data sets of assets, from a multivariate normal, skew normal, or (skew) Student-t distribution.

Usage

assetsSim(n, dim = 2, model = list(mu = rep(0, dim), Omega = diag(dim), 
    alpha = rep(0, dim), df = Inf), assetNames = NULL) 

Arguments

n integer value, the number of data records to be simulated.
dim integer value, the dimension (number of columns) of the assets set.
model a list of model parameters:
mu a vector of mean values, one for each asset series,
Omega the covariance matrix of assets,
alpha the skewness vector, and
df the number of degrees of freedom which is a measure for the fatness of the tails (excess kurtosis).
For a symmetric distribution alpha is a vector of zeros. For the normal distributions df is not used and set to infinity, Inf. Note that all assets have the same value for df.
assetNames [assetsSim] -
a vector of character strings of length dim allowing for modifying the names of the individual assets.

Value

assetsSim()
returns a data.frame of simulated assets.

Author(s)

Adelchi Azzalini for R's sn package,
Torsten Hothorn for R's mtvnorm package,
Diethelm Wuertz for the Rmetrics port.

References

Azzalini A. (1985); A Class of Distributions Which Includes the Normal Ones, Scandinavian Journal of Statistics 12, 171–178.

Azzalini A. (1986); Further Results on a Class of Distributions Which Includes the Normal Ones, Statistica 46, 199–208.

Azzalini A., Dalla Valle A. (1996); The Multivariate Skew-normal Distribution, Biometrika 83, 715–726.

Azzalini A., Capitanio A. (1999); Statistical Applications of the Multivariate Skew-normal Distribution, Journal Roy. Statist. Soc. B61, 579–602.

Azzalini A., Capitanio A. (2003); Distributions Generated by Perturbation of Symmetry with Emphasis on a Multivariate Skew-t Distribution, Journal Roy. Statist. Soc. B65, 367–389.

Genz A., Bretz F. (1999); Numerical Computation of Multivariate t-Probabilities with Application to Power Calculation of Multiple Contrasts, Journal of Statistical Computation and Simulation 63, 361–378.

Genz A. (1992); Numerical Computation of Multivariate Normal Probabilities, Journal of Computational and Graphical Statistics 1, 141–149.

Genz A. (1993); Comparison of Methods for the Computation of Multivariate Normal Probabilities, Computing Science and Statistics 25, 400–405.

Hothorn T., Bretz F., Genz A. (2001); On Multivariate t and Gauss Probabilities in R, R News 1/2, 27–29.

Wuertz, D., Chalabi, Y., Chen W., Ellis A. (2009); Portfolio Optimization with R/Rmetrics, Rmetrics eBook, Rmetrics Association and Finance Online, Zurich.

See Also

MultivariateDistribution.

Examples


## LPP -
   # Percentual Returns:
   LPP = 100 * as.timeSeries(data(LPP2005REC))[, 1:3]
   colnames(LPP)
   
## assetsFit -
   # Fit a Skew-Student-t Distribution:
   fit = assetsFit(LPP)
   print(fit)
   # Show Model Slot:
   print(fit@model)
   
## assetsSim -
   # Simulate set with same statistical properties:
   set.seed(1953)
   lppSim = assetsSim(n = nrow(LPP), dim = ncol(LPP), model = fit@model)
   colnames(lppSim) <- colnames(LPP)
   rownames(lppSim) <- rownames(LPP)
   head(lppSim)
   head(as.timeSeries(lppSim))

[Package fAssets version 2100.77 Index]