sp500.subset

<p>This data set provides a subset of the SP500 with returns suitable
for running against the portfolio optimization routines.</p>

datasets

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.

Brian Lee Yung Rowe

tawny

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

sp500.subset function

<p>A 200x75 zoo object of asset returns</p>

sp500.subset: A subset of the SP500 with 200 data points

Description

Usage

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