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.
- Brian Lee Yung Rowe
- Date of publication
- 2016-07-10 18:59:43
- Brian Lee Yung Rowe <firstname.lastname@example.org>
- Shrink the covariance matrix towards some global mean
- Remove noise from a correlation matrix using RMT to identify...
- Measure the divergence and stability between two correlation...
- Utility functions for creating portfolios of returns and...
- Optimize a portfolio using the specified correlation filter
- A (mostly complete) subset of the SP500 with 250 data points
- A subset of the SP500 with 200 data points
- Clean Covariance Matrices Using Random Matrix Theory and...
Files in this package