muS.sp500 {CLA}

Return Expectation and Covariance for "FRAPO"s SP500 data

Description

If R_{j,t} are the basically the scale standardized log returns for j = 1,2,\dots,476 of 476 stocks from S&P 500, as from SP500, then mu_j = E[R_{j,*}] somehow averaged over time; actually as predicted by muSigma() at the end of the time period, and \Sigma_{j,k} = Cov(R_j, R_k) are estimated covariances.

These are the main “inputs” needed for the CLA algorithm, see CLA.

Usage

data("muS.sp500")

Format

A list with two components,

mu

Named num [1:476] 0.00233 0.0035 0.01209 0.00322 0.00249 ...
names : chr [1:476] "A" "AA" "AAPL" "ABC" ...

covar

num [1:476, 1:476] 0.001498 0.000531 0.000536 ...

Source

It is as simple as this:

    data(SP500, package="FRAPO")
    system.time(muS.sp500 <- muSigmaGarch(SP500))   #   26 sec. (lynne, 2017)
  

See Also

muSigmaGarch() which was used to construct it.

Examples

data(muS.sp500)
str(muS.sp500)