AssetsMeanCovariances {fAssets}R Documentation

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:

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 [assetsMeanCov] -
when methode="MCD", a numeric parameter controlling the size of the subsets over which the determinant is minimized, i.e., alpha*n observations are used for computing the determinant. Allowed values are between 0.5 and 1 and the default is 0.5. For details we refer to the help pages of the R-package robustbase.
baggedR [assetsMeanCov] -
when methode="bagged", an integer value, the number of bootstrap replicates, by default 100.
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 method="shrink" is selected then the covariance will be computed using the shrinkage approach as suggested in Schaefer and Strimmer [2005], if method="bagged" is selected then the covariance will be calculated from the bootstrap aggregated (bagged) version of the covariance estimator.
sigmamu [assetsMeanCov] -
when methode="OGK", a function that computes univariate robust location and scale estimates. By default it should return a single numeric value containing the robust scale (standard deviation) estimate. When mu.too is true (the default), sigmamu() should return a numeric vector of length 2 containing robust location and scale estimates. See scaleTau2, s_Qn, s_Sn, s_mad or s_IQR for examples to be used as sigmamu argument. For details we refer to the help pages of the R-package robustbase.
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.
... [assetsMeanCov] -
optional arguments to be passed to the underlying estimators. For details we refer to the manual pages of the functions cov.rob for arguments "mve" and mcd" in the R package MASS, to the functions covMcd and covOGK in the R package robustbase.

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.

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.

Author(s)

Juliane Schaefer and Korbinian Strimmer for R's corpcov package,
Diethelm Wuertz for the Rmetrics port.

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")

[Package fAssets version 260.72 Index]