Description Usage Arguments Details Value
Fit a statistical factor model using Principal Component Analysis (PCA)
1 | statistical.factor.model(R, k = 1, ...)
|
R |
xts of asset returns |
k |
number of factors to use |
... |
additional arguments passed to
|
The statistical factor model is fitted using
prcomp
. The factor loadings, factor realizations,
and residuals are computed and returned given the number
of factors used for the model.
#'
factor_loadings N x k matrix of factor loadings (i.e. betas)
factor_realizations m x k matrix of factor realizations
residuals m x N matrix of model residuals representing idiosyncratic risk factors
Where N is the number of assets, k is the number of factors, and m is the number of observations.
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