missing data in return series...
There are two functions in the BurStFin
package ('factor.model.stat' and 'var.shrink.eqcor')
that will create variance matrix estimates
when there are missing values in the return
matrix.
The second of those gives Ledoit-Wolf estimates,
and is probably going to give the more useful
results.
I believe that the best way to handle missing values
for these estimates is still an open research question.
The functions handle missing values, no claim that
they do it optimally.
You can get the package via:
install.packages('BurStFin', repos='http://www.burns-stat.com/R')
As for means: historical means are in general not
of much use, so it is unlikely that it will matter
how you estimate them.
On 19/09/2011 08:26, ShyhWeir Tzang wrote:
Dear all:
I have a portfolio of about 50 stocks of which about 10~15 stocks with
unequal lengths. That means they have shorter historical return series than
others. How may I estimate the covariance matrix and mean of the stocks? Is
the Stambaugh (1997) ("Analyzing investments whose histories differ in
length") method still valid for individual stocks instead of funds? Is there
any better way or more efficient way to estimate their mean and covariance
matrix? Any help or suggestion is highly appreciated.
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