Incidentally, one of the functions I attached,

getrffBeta, uses the robust package WLE to calculate

3-factor coefficients. Even if one believes the return

differentials accruing to value-growth and

largecap-smallcap spreads are an anomaly erased

through robust statistics, that does not mean that one

could not include those factors to get a better

calculation of true market beta, does it? You could

take the 3-factor regression output, assign zero

premiums to size and value factors, and use the market

beta that has already controlled for size and value

effects, right?

For most of the companies I've looked at, the AIC on 3

factor models are a lot higher than CAPM models. You

can see it for yourself graphically by running the

getrffBeta function on various stocks. For example,

using CAPM, some of the dramatic swings in the beta of

low-tech stocks (falling dramatically during the

internet surge) may have been more due to value-growth

return trends than a true change in market beta. The 3

factor model thus seems to result in more stable

market beta estimates than the CAPM model.

Regards,

Andrew

--- Krishna Kumar <

[hidden email]> wrote:

> Gabor Grothendieck wrote:

>

> >On 4/6/06, Andrew West <

[hidden email]> wrote:

> >

> >

> >>I haven't been able to figure

> >>out how to prevent the function from crashing when

> one

> >>of the companies in the list has a late start or

> >>missing data. Aligning multiple time series into a

> >>panel data dataframe is tough for non-programmers

> like

> >>me!

> >>

> >>

> >

> >If t1 and t2 are two ts class time series or two

> zoo series

> >then cbind(t1, t2) will create a multivariate

> series (2 columns)

> >In the case of zoo, merge(t1, t2) will also work.

> >

> >na.omit(cbind(t1, t2)) or na.omit(merge(t1,t2))

> >will eliminate rows that have any NAs in the case

> of zoo series.

> >

> >_______________________________________________

> >

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> >

https://stat.ethz.ch/mailman/listinfo/r-sig-finance> >

> >

> >

>

> To add to Gabor's suggestion you could do the

> following to get an

> approximated series..

> so if mydata is a vector with "NA" 's then doing

>

> >mydata<-approx(mydata,xout=seq(along=mydata))$y

>

> this would approximate the series and then you can

> do a ts.union

>

> Also there was a very interesting paper that showed

> that the Fama-French

> effect was not really a anamoly when you estimate

> using

> Robust regression instead of OLS. I can't remember

> the reference but it

> was Doug Martin and someone else from UW ...

> R has some nice facilities with rrcov to do the

> robust regressions!!

>

> Best,

> Krishna

>

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