compareCov

cov1

covariance matrix using the second method

cov2

strings indicating the type of methods used for comparison

labels

flag indicating if the supplied matrices are of type covariance
or correlation

corr


This is a utility function to compare two covariance matrices


We implement a collection of techniques for estimating covariance matrices.
Covariance matrices can be built using missing data. Stambaugh Estimation and
FMMC methods can be used to construct such matrices. Covariance matrices can
be built by denoising or shrinking the eigenvalues of a sample covariance
matrix. Such techniques work by exploiting the tools in Random Matrix Theory
to analyse the distribution of eigenvalues. Covariance matrices can also
be built assuming that data has many underlying regimes. Each regime is
allowed to follow a Dynamic Conditional Correlation model. Robust covariance
matrices can be constructed by multivariate cleaning and smoothing of noisy data.

compareCov: This is a utility function to compare two covariance matrices

Description

Usage

Arguments

Details