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.

Version: 2.1.6
Depends: R (≥ 3.0.0)
Imports: lambda.r (≥ 1.1.6), lambda.tools, futile.logger (≥ 1.3.7), futile.matrix (≥ 1.2.1), tawny.types (≥ 1.1.2), zoo, xts, PerformanceAnalytics, quantmod
Suggests: RUnit
Published: 2016-07-10
Author: Brian Lee Yung Rowe
Maintainer: Brian Lee Yung Rowe <r at zatonovo.com>
License: GPL-3
NeedsCompilation: no
Materials: README
In views: Finance
CRAN checks: tawny results


Reference manual: tawny.pdf
Package source: tawny_2.1.6.tar.gz
Windows binaries: r-devel: tawny_2.1.6.zip, r-release: tawny_2.1.6.zip, r-oldrel: tawny_2.1.6.zip
OS X El Capitan binaries: r-release: tawny_2.1.6.tgz
OS X Mavericks binaries: r-oldrel: tawny_2.1.6.tgz
Old sources: tawny archive


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