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 |

**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...

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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