covmat: Covariance Matrix Estimation

We implement a collection of techniques for estimating covariance matrices. Covariance matrices can be built using missing data. Stambaugh Estimation and FMMC methods can be used to construct such matrices. Covariance matrices can be built by denoising or shrinking the eigenvalues of a sample covariance matrix. Such techniques work by exploiting the tools in Random Matrix Theory to analyse the distribution of eigenvalues. Covariance matrices can also be built assuming that data has many underlying regimes. Each regime is allowed to follow a Dynamic Conditional Correlation model. Robust covariance matrices can be constructed by multivariate cleaning and smoothing of noisy data.

Version: 1.0
Depends: mvtnorm, RMTstat, grid
Imports: zoo, xts, robust, robustbase, VIM, ggplot2, reshape2, Matrix, parallel, doParallel, fGarch, lhs, scales, gridExtra, optimx, DEoptim, foreach
Suggests: knitr, knitcitations, roxygen2, quantmod, PortfolioAnalytics, rmgarch
Published: 2015-09-28
Author: Rohit Arora
Maintainer: Rohit Arora <emailrohitarora at>
License: Artistic-2.0
NeedsCompilation: yes
Materials: README
In views: Finance
CRAN checks: covmat results


Reference manual: covmat.pdf
Vignettes: Vignette Title
Package source: covmat_1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: covmat_1.0.tgz
OS X Mavericks binaries: r-oldrel: covmat_1.0.tgz


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