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 gmail.com> |
| BugReports: |
NA |
| License: |
Artistic-2.0 |
| URL: |
NA |
| NeedsCompilation: |
yes |
| Materials: |
README |
| In views: |
Finance |
| CRAN checks: |
covmat results |