Package: FinCovRegularization 1.1.0

FinCovRegularization: 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.

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

FinCovRegularization_1.1.0.tar.gz
FinCovRegularization_1.1.0.zip(r-4.7)FinCovRegularization_1.1.0.zip(r-4.6)FinCovRegularization_1.1.0.zip(r-4.5)
FinCovRegularization_1.1.0.tgz(r-4.6-any)FinCovRegularization_1.1.0.tgz(r-4.5-any)
FinCovRegularization_1.1.0.tar.gz(r-4.7-any)FinCovRegularization_1.1.0.tar.gz(r-4.6-any)
FinCovRegularization_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION
card.svg |card.png
FinCovRegularization/json (API)

# Install 'FinCovRegularization' in R:
install.packages('FinCovRegularization', repos = c('https://yanyachen.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/yanyachen/fincovregularization/issues

Datasets:

On CRAN:

Conda:

4.40 score 7 stars 1 packages 24 scripts 220 downloads 16 exports 1 dependencies

Last updated from:cd3ff5b5d0. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE99
source / vignettesOK127
linux-release-x86_64NOTE96
macos-release-arm64NOTE176
macos-oldrel-arm64NOTE149
windows-develNOTE75
windows-releaseNOTE59
windows-oldrelNOTE61
wasm-releaseOK93

Exports:bandingbanding.cvF.norm2FundamentalFactor.CovGMVPhard.thresholdingInd.CovMacroFactor.CovO.norm2RiskParitysoft.thresholdingStatFactor.Covtaperingtapering.cvthreshold.cvthreshold.min

Dependencies:quadprog