View source: R/S3_weights_portfol.R
new_GMV_portfolio_weights_BDPS19 | R Documentation |
Constructor of global minimum variance portfolio.
new_GMV_portfolio_weights_BDPS19 is for the case p<n, while
new_GMV_portfolio_weights_BDPS19_pgn is for p>n, where p is the number of
assets and n is the number of observations. For more details
of the method, see MVShrinkPortfolio
.
new_GMV_portfolio_weights_BDPS19(x, b, beta)
new_GMV_portfolio_weights_BDPS19_pgn(x, b, beta)
x |
a p by n matrix or a data frame of asset returns. Rows represent different assets, columns – observations. |
b |
a numeric vector. 1-beta is the confidence level of the symmetric confidence interval, constructed for each weight. |
beta |
a numeric variable. The confidence level for weight intervals. |
an object of class MeanVar_portfolio with subclass GMV_portfolio_weights_BDPS19.
Element | Description |
call | the function call with which it was created |
cov_mtrx | the sample covariance matrix of the asset returns |
inv_cov_mtrx | the inverse of the sample covariance matrix |
means | sample mean vector estimate of the asset returns |
w_GMVP | sample estimator of portfolio weights |
weights | shrinkage estimator of portfolio weights |
alpha | shrinkage intensity for the weights |
Port_Var | portfolio variance |
Port_mean_return | expected portfolio return |
Sharpe | portfolio Sharpe ratio |
weight_intervals | A data frame, see details |
weight_intervals contains a shrinkage estimator of portfolio weights, asymptotic confidence intervals for the true portfolio weights, the value of test statistic and the p-value of the test on the equality of the weight of each individual asset to zero \insertCite@see Section 4.3 of @CORRBDNT23HDShOP. weight_intervals is only computed when p<n.
BDPS2019HDShOP
\insertRefBPS2018HDShOP
\insertRefCORRBDNT23HDShOP
# c<1
n <- 3e2 # number of realizations
p <- .5*n # number of assets
b <- rep(1/p,p)
# Assets with a diagonal covariance matrix
x <- matrix(data = rnorm(n*p), nrow = p, ncol = n)
test <- new_GMV_portfolio_weights_BDPS19(x=x, b=b, beta=0.05)
str(test)
# Assets with a non-diagonal covariance matrix
Mtrx <- RandCovMtrx(p=p)
x <- t(MASS::mvrnorm(n=n , mu=rep(0,p), Sigma=Mtrx))
test <- new_GMV_portfolio_weights_BDPS19(x=x, b=b, beta=0.05)
summary(test)
# c>1
p <- 1.3*n # number of assets
b <- rep(1/p,p)
# Assets with a diagonal covariance matrix
x <- matrix(data = rnorm(n*p), nrow = p, ncol = n)
test <- new_GMV_portfolio_weights_BDPS19_pgn(x=x, b=b, beta=0.05)
str(test)
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