fPortfolio (version 251.70)

AssetsMeanCovariances: Estimation of Mean and Covariances of Asset Sets

Description

A collection and description of functions which allow to estimate the mean and/or covariance matrix of a time series of assets by traditional and robust methods. The functions are: ll{ assetsStats Computes basic statistics of a set of assets, assetsMeanCov Computes mean and covariance matrix. }

Usage

assetsStats(x)
assetsMeanCov(x, method = c("cov", "mve", "mcd", "MCD", "OGK", "nnve", 
    "shrink", "bagged"), check = TRUE, force = TRUE, baggedR = 100, 
    sigmamu = scaleTau2, alpha = 1/2, ...)

Arguments

alpha

...

baggedR

an integer value, the number of bootstrap replicates, by default 100. This value is only used if method="bagged".

check

a logical flag. Should the covariance matrix be tested to be positive definite? By default TRUE.

force

[assetsMeanCov] - a logical flag. Should the covariance matrix be forced to be positive definite? By default TRUE.

method

[assetsMeanVar] - a character string, whicht determines how to compute the covariance matix. If method="cov" is selected then the standard covariance will be computed by R's base function cov, if

sigmamu

...

x

any rectangular time series object which can be converted by the function as.matrix() into a matrix object, e.g. like an object of class timeSeries, data.frame, or mts.

...

optional arguments to be passed.

Value

• assetsMeanCov returns a list with two entries named mu and Sigma{Sigma}. The first denotes the vector of assets means, and the second the covariance matrix. Note, that the output of this function can be used as data input for the portfolio functions to compute the efficient frontier.

Details

Assets Mean and Covariance: The function assetsMeanCov computes the mean vector and covariance matrix of an assets set. For the covariance matrix one can select from three choicdes: The standard covariance computation through R's base function cov and a shrinked and bagged version for the covariance. The latter two choices implement the covariance computation from the functions cov.shrink() and cov.bagged() which are part of the contributed R package corpcov.

References

Breiman L. (1996); Bagging Predictors, Machine Learning 24, 123--140.

Ledoit O., Wolf. M. (2003); ImprovedEestimation of the Covariance Matrix of Stock Returns with an Application to Portfolio Selection, Journal of Empirical Finance 10, 503--621.

Schaefer J., Strimmer K. (2005); A Shrinkage Approach to Large-Scale Covariance Estimation and Implications for Functional Genomics, Statist. Appl. Genet. Mol. Biol. 4, 32.

See Also

MultivariateDistribution.

Examples

## berndtInvest -
   data(berndtInvest)
   # Select "CONTIL" "DATGEN" "TANDY" and "DEC" Stocks:
   select = c("CONTIL", "DATGEN", "TANDY", "DEC")
   # Convert into a timeSeries object:
   berndtAssets.tS = as.timeSeries(berndtInvest)[, select]
   head(berndtAssets.tS)
   
## Classical Covariance Estimation:
   assetsMeanCov(berndtAssets.tS, method = "cov")
   
## mcd Covariance Estimation:
   # assetsMeanCov(berndtAssets.tS, method = "mcd")
   
## mve Covariance Estimation:
   # assetsMeanCov(berndtAssets.tS, method = "mve")
   
## nnve Covariance Estimation:
   # assetsMeanCov(berndtAssets.tS, method = "nnve")
   
## shrinkage Covariance Estimation:
   assetsMeanCov(berndtAssets.tS, method = "shrink")
   
## bagged Covariance Estimation:
   assetsMeanCov(berndtAssets.tS, method = "bagged")