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FinCovRegularization (version 1.0.0)

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.packages('FinCovRegularization')

Monthly Downloads

174

Version

1.0.0

License

GPL-2

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Maintainer

Yachen Yan

Last Published

March 1st, 2015

Functions in FinCovRegularization (1.0.0)

MacroFactor.Cov

Covariance Matrix Estimation by Macroeconomic Factor Model
banding

Banding Opreator on Covariance Matrix
F.norm2

The Squared Frobenius Norm
Ind.Cov

Independence opreator on Covariance Matrix
threshold.min

Minimum threshold constant
RiskParity

Risk Parity Portfolio
GMVP

Global Minimum Variance Portfolio
hard.thresholding

Hard-Thresholding Opreator on Covariance Matrix
plot.CovCv

plot CovCv object
tapering.cv

Select Tuning Parameter for Tapering Covariance Matrix by CV
FundamentalFactor.Cov

Covariance Matrix Estimation by Fundamental Factor Model
summary.CovCv

Display a useful description of a CovCv object
m.excess.c10sp9003

10 stock and S&P 500 excess returns
soft.thresholding

Soft-Thresholding Opreator on Covariance Matrix
print.CovCv

print CovCv object
banding.cv

Select Tuning Parameter for Banding Covariance Matrix by CV
tapering

Tapering Opreator on Covariance Matrix
O.norm2

The Squared Operator Norm
threshold.cv

Select Tuning Parameter for Thresholding Covariance Matrix by CV
StatFactor.Cov

Covariance Matrix Estimation by Statistical Factor Model
FinCovRegularization

FinCovRegularization: Covariance Matrix Estimation and Regularization for Finance