| Usage: |
VaR = VaRpred(y{,method{,opt}})
|
| Input: |
| y | n x d matrix, the returns of d assets.
|
| method | method for VaR, one of
"BETA" (beta factor model),
"BETACOR" (corrected beta factor model),
"RE" (model based on random effects: common correlation),
"PRINC" (principal components),
"PRINCW" (weighted principal components).
"SUMS" (sums of uncorrelated variables).
"COMPLETE" (no reduction of variance matrix).
default is the "COMPLETE" option, the beta
factor models assume that the first column
in the data matrix is the index.
Model RE needs at least two-dimensional data.
|
| opt | optional, a list with optional input. The macro
"VaRopt" can be used to set up this parameter.
The order of the list elements is not important.
|
| opt.alpha | scalar in (0,1), significance level. The
default is 0.01.
|
| opt.w | scalar, 1 x d or n+1 x d, weights for assets.
If not given, set to 1, the first coordinate
is not used in computation for beta factor models.
|
| Output: |
| VaR | 1 x 2 vector, the VaR for observation n+1. |