Covariance Matrix Estimation and Regularization for Finance

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.


Covariance Matrix Estimation and Regularization for Finance

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.

  • covariance estimation: macroeconomic factor model, fundamental factor model and statistical factor model
  • covariance regularization: banding, tapering, hard-thresholding, soft-thresholding
  • portfolio optimization: global mimnum variance portfolio, risk parity portfolio

To install:

  • the latest development version: devtools::install_github("yanyachen/FinCovRegularization")

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Reference manual

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install.packages("FinCovRegularization")

1.1.0 by YaChen Yan, 5 years ago


http://github.com/yanyachen/FinCovRegularization


Report a bug at http://github.com/yanyachen/FinCovRegularization/issues


Browse source code at https://github.com/cran/FinCovRegularization


Authors: YaChen Yan [aut, cre] , FangZhu Lin [aut]


Documentation:   PDF Manual  


GPL-2 license


Imports stats, graphics, quadprog


See at CRAN