sigma_estim_afm: Approximate Factor Model (AFM) Covariance Estimation

In antshi/CovEstim: (High-dimensional) Covariance Estimation

Description

Computes the AFM estimator of the covariance matrix.

Usage

sigma_estim_afm(
  data,
  factors = NULL,
  zeromean_log = FALSE,
  resid_est_func,
  ...
)

Arguments

data	an nxp data matrix.
factors	a nxf matrix with factors. Default value is NULL and the factor is equal to the cross-sectional average of all the variables in the data.
zeromean_log	a logical, indicating whether the data matrix has zero means (TRUE) or not (FALSE). Default value is FALSE.
resid_est_func	a covariance estimation function, applied to the residuals covariance matrix.
...	further arguments to be parsed to resid_est_func

Details

The AFM covariance estimator is calculated with the following formula:

\hat{Σ} = B\hat{Σ}_F B' + \hat{Σ_u},

where \hat{Σ}_F is the sample covariance matrix of the common factors and \hat{Σ}_u is the residuals covariance matrix, estimated with the user-sapplied estim_func.

Value

a list with the following entries

• a pxp estimated covariance matrix.

• an estimation specific tuning parameter, depending on the estimation function type.

Examples

data(sp200)
sp_rets <- sp200[,-1]
sigma_afm <- sigma_estim_afm(sp_rets, resid_est_func=sigma_estim_lwnl)[[1]]
results_afm <- sigma_estim_afm(sp_rets, resid_est_func=sigma_estim_lwone, shrink_int=0.1)
sigma_afm <- results_afm[[1]]
param_afm <- results_afm[[2]]

antshi/CovEstim documentation built on Nov. 13, 2020, 2:25 p.m.