New answers tagged optimization
0
Just add the k=6 to the endpoints() wrapper and use that function instead...
optimize.portfolio.rebalancing2 = function (R, portfolio = NULL, constraints = NULL, objectives = NULL,
optimize_method = c("DEoptim", "random", "ROI"), search_size = 20000,
trace = FALSE, ..., rp = NULL, rebalance_on = NULL, training_period = NULL,
...
1
Check this out:
library(quadprog)
Dmat <- matrix(0,15,15)
diag(Dmat) <- 1
dvec <- matrix(0,1,15)
bvec <- c(-0.02269294, 0.07120749, -0.01830448, 0.04465172, 0.03508689,
-0.0003176476, 0.01089419, 0.06466093, 0.01265293, -0.02748855,
0.04753743, -0.000408749, 0.03108376, -0.07378021, -0.0608137,
1, 0, 0, 0, ...
1
In theory, the Ledoit and Wolf shrinkage estimator is supposed to guarantee a positive-definite matrix, given that it adds a positive-definite matrix (the target) to a semi-positive one (the sample covariance).
I can see four reasons why you didn't get a positive-definite matrix:
Your true covariance is effectively not full rank, i..e you have perfect ...
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