Views on Relative Performance — view_on

Helper to construct views on relative performance of assets.

Usage

view_on_rank(x, rank)

# S3 method for default
view_on_rank(x, rank)

# S3 method for matrix
view_on_rank(x, rank)

# S3 method for xts
view_on_rank(x, rank)

# S3 method for tbl_df
view_on_rank(x, rank)

Arguments

x: An univariate or a multivariate distribution.
rank: A integer with the assets rank (from the worst to the best performer).

Value

A list of the view class.

Details

If rank = c(2, 1) it is implied that asset in the first column will outperform the asset in the second column. For longer vectors the interpretation is the same: assets on the right will outperform assets on the left.

Examples

library(ggplot2)

# Invariants
x <- diff(log(EuStockMarkets))
prior <- rep(1 / nrow(x), nrow(x))

# asset in the first col will outperform the asset in the second col (DAX will
# outperform SMI).
views <- view_on_rank(x = x, rank = c(2, 1))
views
#> # ffp view
#> Type:  View On Rank
#> A :  Dim 1 x 1859 
#> b :  Dim 1 x 1 

ep <- entropy_pooling(p = prior, A = views$A, b = views$b, solver = "nloptr")
autoplot(ep)


# Prior Returns (SMI > DAX)
colMeans(x)[1:2]
#>          DAX          SMI 
#> 0.0006520417 0.0008178997 

# Posterior Returns (DAX > SMI)
ffp_moments(x, ep)$mu[1:2]
#>          DAX          SMI 
#> 0.0007646772 0.0007646772