denoise: Remove noise from a correlation matrix using RMT to identify...

In zatonovo/tawny: Clean Covariance Matrices Using Random Matrix Theory and Shrinkage Estimators for Portfolio Optimization

Description

Used to filter out all eigenvalues below k*. At a later date this will become pluggable so other people can use their own functions and/or provide their own parameters to this function.

Usage

denoise(...)
cor.empirical(h)
cor.clean(es, lambda.plus=1.6, h=NULL)
normalize(...)

Arguments

h	A returns object
es	Eigenvalues and vectors
lambda.plus	Eigenvalue cutoff for cleaning
...	Arguments

Details

denoise(p, estimator)

normalize(h)

These are different implementations of the denoise function based on the estimator provided.

Value

A cleaned correlation matrix.

Author(s)

Brian Lee Yung Rowe

See Also

tawny, optimizePortfolio

Examples

## Not run: 
data(sp500.subset)

h <- sp500.subset
p <- TawnyPortfolio(h, 150)
r1 <- denoise(p, SampleDenoiser())
r2 <- denoise(p, EmpiricalDenoiser())
r3 <- denoise(p, ShrinkageDenoiser())
r4 <- denoise(p, RandomMatrixDenoiser())

## End(Not run)

zatonovo/tawny documentation built on May 4, 2019, 9:12 p.m.