tawny: Clean Covariance Matrices Using Random Matrix Theory and Shrinkage Estimators for Portfolio Optimization

Portfolio optimization typically requires an estimate of a covariance matrix of asset returns. There are many approaches for constructing such a covariance matrix, some using the sample covariance matrix as a starting point. This package provides implementations for two such methods: random matrix theory and shrinkage estimation. Each method attempts to clean or remove noise related to the sampling process from the sample covariance matrix.

Author
Brian Lee Yung Rowe
Date of publication
2016-07-10 18:59:43
Maintainer
Brian Lee Yung Rowe <r@zatonovo.com>
License
GPL-3
Version
2.1.6

View on CRAN

Man pages

cov_shrink
Shrink the covariance matrix towards some global mean
denoise
Remove noise from a correlation matrix using RMT to identify...
divergence
Measure the divergence and stability between two correlation...
getPortfolioReturns
Utility functions for creating portfolios of returns and...
optimizePortfolio
Optimize a portfolio using the specified correlation filter
sp500
A (mostly complete) subset of the SP500 with 250 data points
sp500.subset
A subset of the SP500 with 200 data points
tawny-package
Clean Covariance Matrices Using Random Matrix Theory and...

Files in this package

tawny
tawny/inst
tawny/inst/unitTests
tawny/inst/unitTests/runit.crud.R
tawny/inst/unitTests/runit.shrinkage.R
tawny/tests
tawny/tests/doRUnit.R
tawny/NAMESPACE
tawny/data
tawny/data/sp500.RData
tawny/data/sp500.subset.RData
tawny/R
tawny/R/divergence.R
tawny/R/shrinkage.R
tawny/R/framework.R
tawny/R/denoise.R
tawny/R/util.R
tawny/README.md
tawny/MD5
tawny/DESCRIPTION
tawny/man
tawny/man/cov_shrink.Rd
tawny/man/sp500.subset.Rd
tawny/man/optimizePortfolio.Rd
tawny/man/divergence.Rd
tawny/man/tawny-package.Rd
tawny/man/sp500.Rd
tawny/man/denoise.Rd
tawny/man/getPortfolioReturns.Rd