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.

AuthorBrian Lee Yung Rowe
Date of publication2016-07-10 18:59:43
MaintainerBrian Lee Yung Rowe <r@zatonovo.com>
LicenseGPL-3
Version2.1.6

View on CRAN

Files

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

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