builtin: Estimation of Mean and Covariances of Asset Sets

Description

Helper functions for estimating the mean and/or covariance matrix of a time series of assets by traditional and robust methods.

Usage

.baggedMeanCov(x, baggedR = 100, ...)
.bayesSteinMeanCov(x, ...)
.cov.arw(x, center, cov, alpha = 0.025, pcrit = NULL)
.cov.nnve(datamat, k = 12, pnoise = 0.05, emconv = 0.001, bound = 1.5,
extension = TRUE, devsm = 0.01)
.cov.shrink(x, lambda, verbose = FALSE)
.donostahMeanCov(x, ...)
.ledoitWolfMeanCov(x, ...)
.rmtMeanCov(x, ...)
.studentMeanCov(x, ...)

Value

The functions return a list with elements containing the covariance and mean. The list may contain additional control parameters.

Arguments

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.
baggedR: when methode="bagged", an integer value, the number of bootstrap replicates, by default 100.
center: specifies for a data set (n x p), the initial location estimator(1 x p).
cov: Initial scatter estimator (p x p).
alpha: Maximum thresholding proportion (optional scalar, default: alpha = 0.025).
pcrit: critical value for outlier probability (optional scalar, default values from simulations).
datamat: a matrix in which each row represents an observation or point and each column represents a variable.
k: desired number of nearest neighbors (default is 12).
pnoise: percent of added noise
emconv: convergence tolerance for EM.
bound: value used to identify surges in variance caused by outliers wrongly included as signal points (bound = 1.5 means a 50 percent increase).
extension: whether or not to continue after reaching the last chi-square distance. The default is to continue, which is indicated by setting extension= TRUE.
devsm: when extension = TRUE, the algorithm stops if the relative difference in variance is less than devsm (default is 0.01).
lambda: the correlation shrinkage intensity (range 0-1). If lambda is not specified (the default) it is estimated using an analytic formula from Schaefer and Strimmer (2005) - see details below. For lambda=0 the empirical correlations are recovered.
verbose: a logical indicating whether to print progress information to the stdout.
...: optional arguments to be passed to the underlying estimators. For details we refer to the manual pages of the functions cov.rob in the R package MASS, to the functions covMcd and covOGK in the R package robustbase.