## File: assets-meancov.Rd

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fassets 3011.83-2
 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163 \name{assets-meancov} \alias{assetsMeanCov} \alias{getCenterRob} \alias{getCovRob} \title{Estimation of Mean and Covariances of Asset Sets} \description{ Estimates the mean and/or covariance matrix of a time series of assets by traditional and robust methods. } \usage{ assetsMeanCov(x, method = c("cov", "mve", "mcd", "MCD", "OGK", "nnve", "shrink", "bagged"), check = TRUE, force = TRUE, baggedR = 100, sigmamu = scaleTau2, alpha = 1/2, ...) getCenterRob(object) getCovRob(object) } \arguments{ \item{x}{ any rectangular time series object which can be converted by the function \code{as.matrix()} into a matrix object, e.g. like an object of class \code{timeSeries}, \code{data.frame}, or \code{mts}. } \item{method}{ a character string, whicht determines how to compute the covariance matix. If \code{method="cov"} is selected then the standard covariance will be computed by R's base function \code{cov}, if \code{method="shrink"} is selected then the covariance will be computed using the shrinkage approach as suggested in Schaefer and Strimmer [2005], if \code{method="bagged"} is selected then the covariance will be calculated from the bootstrap aggregated (bagged) version of the covariance estimator. } \item{check}{ a logical flag. Should the covariance matrix be tested to be positive definite? By default \code{TRUE}. } \item{force}{ a logical flag. Should the covariance matrix be forced to be positive definite? By default \code{TRUE}. } \item{baggedR}{ when \code{methode="bagged"}, an integer value, the number of bootstrap replicates, by default 100. } \item{sigmamu}{ when \code{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 \code{mu.too} is true (the default), \code{sigmamu()} should return a numeric vector of length 2 containing robust location and scale estimates. See \code{scaleTau2}, \code{s_Qn}, \code{s_Sn}, \code{s_mad} or \code{s_IQR} for examples to be used as \code{sigmamu} argument. For details we refer to the help pages of the R-package \code{robustbase}. } \item{object}{ a list as returned by the function \code{assetsMeanCov}. } \item{alpha}{ when \code{methode="MCD"}, a numeric parameter controlling the size of the subsets over which the determinant is minimized, i.e., \code{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 \code{robustbase}. } \item{\dots}{ optional arguments to be passed to the underlying estimators. For details we refer to the manual pages of the functions \code{cov.rob} for arguments \code{"mve"} and \code{"mcd"} in the R package \code{MASS}, to the functions \code{covMcd} and \code{covOGK} in the R package \code{robustbase}. } } \value{ \code{assetsMeanCov} returns a list with for entries named \code{center} \code{cov}, \code{mu} and \code{Sigma}. The list may have a character vector attributed with additional control parameters. \code{getCenterRob} extracts the center from an object as returned by the function \code{assetsMeanCov}. \code{getCovRob} extracts the covariance from an object as returned by the function \code{assetsMeanCov}. } \references{ Breiman L. (1996); \emph{Bagging Predictors}, Machine Learning 24, 123--140. Ledoit O., Wolf. M. (2003); \emph{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); \emph{A Shrinkage Approach to Large-Scale Covariance Estimation and Implications for Functional Genomics}, Statist. Appl. Genet. Mol. Biol. 4, 32. Wuertz, D., Chalabi, Y., Chen W., Ellis A. (2009); \emph{Portfolio Optimization with R/Rmetrics}, Rmetrics eBook, Rmetrics Association and Finance Online, Zurich. } \author{ Juliane Schaefer and Korbinian Strimmer for R's \code{corpcov} package, \cr Diethelm Wuertz for the Rmetrics port. } \examples{ ## LPP - LPP <- as.timeSeries(data(LPP2005REC))[, 1:6] colnames(LPP) ## Sample Covariance Estimation: assetsMeanCov(LPP) ## Shrinked Estimation: shrink <- assetsMeanCov(LPP, "shrink") shrink ## Extract Covariance Matrix: getCovRob(shrink) } \keyword{models}