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In GuanglinHuang/hofa: higher-order multi-cumulant factor analysis
hofa R Documentation
hofa: Econometric tools for higher-order multi-cumulant factor analysis
Description
This R package implements several factor analysis approaches based on the covariance matrix and the higher-order multi-cumulants, including factor number selection, factor estimation and the applications in financial market.
Factor number selection
The first major part of the project is about determining the number of factors, hofa implements several approaches based on the covariance matrix and the higher-order moments.
The covariance-based approaches: Bai and Ng(2002)'s Information Criterion(IC3,PC3 and BIC3), Onatski(2010)'s Empirical Distribution method(ON), Ahn and Horenstein(2013)'s Eigenvalue Ratio test(ER and GR), Fan et al.(2020)'s Adjusted Correlation Threshold method(ACT). These methods are compiled in M2.select function.
The higher-order moment-based approaches: Lu et al.(2021)'s Generalized Eigenvalue Ratio test(GER3,GER4,GGR3 and GGR4), Jondeau et al.(2018)'s Threshold method(JJR). These methods are compiled in M3.select and M4.select functions.
Factor estimation
The second major part of the project is about factor estimation, hofa also implements several approaches based on the covariance matrix and the higher-order moments.
The covariance-based approaches contain three parts: Principal Component methods, Maximum Likelihood methods and Generalized Moment methods. The M2.pca function implements classical PCA and Fan et al.(2016)'s Projected PCA(P-PCA). The M2.mle function implements Bai and Li(2012,2013)'s Maximum Likelihood estimation(ML), Quasi Maximum Likelihood estimation(QML), Generalized Least Square algorithm(ML-GLS), Iterative Generalized Least Square algorithm(ML-ITE) and EM algorithm(ML-EM). The M2.gmm function implement Fan and Zhong(2018)'s Generalized Moment Method(GMM).
The higher-order moment-based approaches: Lu et al.(2021)'s Alternating Least Squares algorithm(M3.als and M4.als), Fan and Zhong(2018)'s Generalized Moment Method (M3.gmm, add third-order moment as structure equations).
Portfolio selection
The third part of hofa is about portfolio selection based on the higher-order moments, Lassance and Vrins(2020)'s Independent Component(IC) portfolio and Principal Component(PC) portfolio are implemented in Portfolio.IC and Portfolio.PC functions, respectively.
GuanglinHuang/hofa documentation built on Sept. 3, 2023, 7:01 a.m.
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| hofa | R Documentation |
hofa: Econometric tools for higher-order multi-cumulant factor analysis
Description
This R package implements several factor analysis approaches based on the covariance matrix and the higher-order multi-cumulants, including factor number selection, factor estimation and the applications in financial market.
Factor number selection
The first major part of the project is about determining the number of factors, hofa implements several approaches based on the covariance matrix and the higher-order moments.
The covariance-based approaches: Bai and Ng(2002)'s Information Criterion(IC3,PC3 and BIC3), Onatski(2010)'s Empirical Distribution method(ON), Ahn and Horenstein(2013)'s Eigenvalue Ratio test(ER and GR), Fan et al.(2020)'s Adjusted Correlation Threshold method(ACT). These methods are compiled in M2.select function.
The higher-order moment-based approaches: Lu et al.(2021)'s Generalized Eigenvalue Ratio test(GER3,GER4,GGR3 and GGR4), Jondeau et al.(2018)'s Threshold method(JJR). These methods are compiled in M3.select and M4.select functions.
Factor estimation
The second major part of the project is about factor estimation, hofa also implements several approaches based on the covariance matrix and the higher-order moments.
The covariance-based approaches contain three parts: Principal Component methods, Maximum Likelihood methods and Generalized Moment methods. The M2.pca function implements classical PCA and Fan et al.(2016)'s Projected PCA(P-PCA). The M2.mle function implements Bai and Li(2012,2013)'s Maximum Likelihood estimation(ML), Quasi Maximum Likelihood estimation(QML), Generalized Least Square algorithm(ML-GLS), Iterative Generalized Least Square algorithm(ML-ITE) and EM algorithm(ML-EM). The M2.gmm function implement Fan and Zhong(2018)'s Generalized Moment Method(GMM).
The higher-order moment-based approaches: Lu et al.(2021)'s Alternating Least Squares algorithm(M3.als and M4.als), Fan and Zhong(2018)'s Generalized Moment Method (M3.gmm, add third-order moment as structure equations).
Portfolio selection
The third part of hofa is about portfolio selection based on the higher-order moments, Lassance and Vrins(2020)'s Independent Component(IC) portfolio and Principal Component(PC) portfolio are implemented in Portfolio.IC and Portfolio.PC functions, respectively.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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