Okay, it seems to work with Mahalanobis:
b = (X′S^−1X)−1X′S^−1Y minimizes the Mahalanobis-distance of Xb to Y .
And S is the covariance-matrix.
cov = a0+a1x_{n-1}*y_{n-1}+ß*cov{n-1}
But shouldn´t it be the covariance of the residuals?
Anyone experiences with that?
--
View this message in context:
http://r.789695.n4.nabble.com/CAPM-GARCH-Regression-analysis-with-heteroskedasticity-tp4105346p4106088.html
Sent from the R help mailing list archive at Nabble.com.
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
