AssetsMeanCovariances {fAssets} | R Documentation |
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. |
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, ...)
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 .
|
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
.
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.
Juliane Schaefer and Korbinian Strimmer for R's corpcov
package,
Diethelm Wuertz for the Rmetrics port.
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.
MultivariateDistribution
.
## 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")