mean_bop19 | R Documentation |
Shrinkage estimator of the high-dimensional mean vector as suggested in \insertCiteBOP2019;textualHDShOP. It uses the formula
\hat μ_{BOP} = \hat α \bar x + \hat β μ_0 \quad ,
where \hat α and \hat β are shrinkage coefficients given by Eq.(6) and Eg.(7) of \insertCiteBOP2019;textualHDShOP that minimize weighted quadratic loss for a given target vector μ_0 (shrinkage target). \bar x stands for the sample mean vector.
mean_bop19(x, mu_0 = rep(1, p))
x |
a p by n matrix or a data frame of asset returns. Rows represent different assets, columns – observations. |
mu_0 |
a numeric vector. The target vector used in the construction of the shrinkage estimator. |
a numeric vector containing the shrinkage estimator of the mean vector
n<-7e2 # number of realizations p<-.5*n # number of assets x <- matrix(data = rnorm(n*p), nrow = p, ncol = n) mm <- mean_bop19(x=x)
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