sm_vcov: Compute variance covariance of 'Unified' Second Moment
In shabbychef/SharpeR: Statistical Significance of the Sharpe Ratio

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Computes the variance covariance matrix of sample mean and second moment.

1	sm_vcov(X,vcov.func=vcov,fit.intercept=TRUE)

`X`	an n x p matrix of observed returns.
`vcov.func`	a function which takes an object of class `lm`, and computes a variance-covariance matrix. If equal to the string "normal", we assume multivariate normal returns.
`fit.intercept`	a boolean controlling whether we add a column of ones to the data, or fit the raw uncentered second moment.

Given p-vector x, the 'unified' sample is the p(p+3)/2 vector of x stacked on top of vech(x x'). Given n contemporaneous observations of p-vectors, stacked as rows in the n x p matrix X, this function computes the mean and the variance-covariance matrix of the 'unified' sample.

One may use the default method for computing covariance, via the vcov function, or via a 'fancy' estimator, like sandwich:vcovHAC, sandwich:vcovHC, etc.

a list containing the following components:

`mu`	a q = p(p+3)/2 vector of the mean, then the vech'd second moment of the sample data
`Ohat`	the q x q estimated variance covariance matrix. Only the informative part is returned: one may assume a row and column of zeros in the upper left.
`n`	the number of rows in `X`.
`p`	the number of assets.

This function will be deprecated in future releases of this package. Users should migrate at that time to a similar function in the MarkowitzR package.

Steven E. Pav shabbychef@gmail.com

Pav, S. E. "Asymptotic Distribution of the Markowitz Portfolio." 2013 https://arxiv.org/abs/1312.0557

ism_vcov, sr_vcov

X <- matrix(rnorm(1000*3),ncol=3)
Sigmas <- sm_vcov(X)
Sigmas.n <- sm_vcov(X,vcov.func="normal")
Sigmas.n <- sm_vcov(X,fit.intercept=FALSE)

# make it fat tailed:
X <- matrix(rt(1000*3,df=5),ncol=3)
Sigmas <- sm_vcov(X)

if (require(sandwich)) {
 Sigmas <- sm_vcov(X,vcov.func=vcovHC)
}

# add some autocorrelation to X
Xf <- filter(X,c(0.2),"recursive")
colnames(Xf) <- colnames(X)
Sigmas <- sm_vcov(Xf)

if (require(sandwich)) {
Sigmas <- sm_vcov(Xf,vcov.func=vcovHAC)
}