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 <email@example.com>|
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...