plot_frontier: Plot the Bayesian efficient frontier...

In HDShOP: High-Dimensional Shrinkage Optimal Portfolios

Plot the Bayesian efficient frontier \insertCitebauder21HDShOP and the provided portfolios.

Description

The plotted Bayesian efficient frontier is provided by \insertCite@Eq. (8) in @bauder21;textualHDShOP. It is the set of optimal portfolios obtained by employing the posterior predictive distribution on the asset returns. This efficient frontier can be used to assess the mean-variance efficiency of various estimators of the portfolio weights. The standard deviation of the portfolio return is plotted in the x-axis and the mean portfolio return in the y-axis. The portfolios with the weights \rm{w} are added to the plot by computing \sqrt{\rm{w}^\prime S w} and \rm w^\prime \bar x.

Usage

plot_frontier(x, weights.eff = rep(1/nrow(x), length = nrow(x)))

Arguments

x	a p by n matrix or a data frame of asset returns. Rows represent different assets, columns – observations.
weights.eff	matrix of portfolio weights. Each column contains p values of the weights for a given portfolio. Default: equally weighted portfolio.

Value

a ggplot object

References

\insertAllCited

Examples

p <- 150
n <- 300
gamma <- 10
mu <- seq(0.2,-0.2, length.out=p)
Sigma <- RandCovMtrx(p=p)

x <- t(MASS::mvrnorm(n=n , mu=mu, Sigma=Sigma))

EW_port <- rep(1/p, length=p)
MV_shr_port <- new_MV_portfolio_weights_BDOPS21(x=x, gamma=gamma,
                                                b=EW_port, beta=0.05)$weights
GMV_shr_port <- new_MV_portfolio_weights_BDOPS21(x=x, gamma=Inf, b=EW_port,
                                                 beta=0.05)$weights
MV_trad_port <- new_MV_portfolio_traditional(x=x, gamma=gamma)$weights
GMV_trad_port <- new_MV_portfolio_traditional(x=x, gamma=Inf)$weights

weights.eff <- cbind(EW_port, MV_shr_port, GMV_shr_port,
                     MV_trad_port, GMV_trad_port)
colnames(weights.eff) <- c("EW", "MV_shr", "GMV_shr", "MV_trad", "GMV_trad")


Fplot <- plot_frontier(x, weights.eff)
Fplot

HDShOP documentation built on May 29, 2024, 2:20 a.m.