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FinCovRegularization (version 1.1.0)

Covariance Matrix Estimation and Regularization for Finance

Description

Estimation and regularization for covariance matrix of asset returns. For covariance matrix estimation, three major types of factor models are included: macroeconomic factor model, fundamental factor model and statistical factor model. For covariance matrix regularization, four regularized estimators are included: banding, tapering, hard-thresholding and soft- thresholding. The tuning parameters of these regularized estimators are selected via cross-validation.

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Install

install.packages('FinCovRegularization')

Monthly Downloads

165

Version

1.1.0

License

GPL-2

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Repository

http://github.com/yanyachen/FinCovRegularization

Maintainer

Last Published

April 25th, 2016

Functions in FinCovRegularization (1.1.0)

Select Tuning Parameter for Banding Covariance Matrix by CV

Global Minimum Variance Portfolio

Banding Opreator on Covariance Matrix

Select Tuning Parameter for Tapering Covariance Matrix by CV

Display a useful description of a CovCv object

FinCovRegularization

FinCovRegularization: Covariance Matrix Estimation and Regularization for Finance

hard.thresholding

Hard-Thresholding Opreator on Covariance Matrix

soft.thresholding

Soft-Thresholding Opreator on Covariance Matrix

Risk Parity Portfolio

Independence opreator on Covariance Matrix

Select Tuning Parameter for Thresholding Covariance Matrix by CV

Tapering Opreator on Covariance Matrix

The Squared Operator Norm

Covariance Matrix Estimation by Statistical Factor Model

plot CovCv object

MacroFactor.Cov

Covariance Matrix Estimation by Macroeconomic Factor Model

Minimum threshold constant

m.excess.c10sp9003

10 stock and S&P 500 excess returns

The Squared Frobenius Norm

print CovCv object

FundamentalFactor.Cov

Covariance Matrix Estimation by Fundamental Factor Model