Hi: Thanks for the correction and reference. Eric uses monthly returns in
the example
in his book and I would think that using daily data would result in very
unstable betas but I've been wrong before. Hopefully others can comment.
Mark
On Fri, May 25, 2012 at 12:44 PM, and_mue wrote:
> Fo
For the analysis I follow the approach of Keown & Pinkerton (
http://e-m-h.org/KePi81.pdf http://e-m-h.org/KePi81.pdf ). They do also use
daily data to compute alphas and betas of the market model. These estimated
coefficients are then used to estimate abnormal returns for a given period.
market
Hi: I don't have time to look at it carefully but, at a glance, you're not
getting a significant
ror_spi_resn coeffficent so worrying about residuals being auto-correlated
is jumping
the gun because you're not really filtering anything in the first place.
when you say, "market model", I don't know
Hi,
I have a problem with a regression I try to run. I did an estimation of the
market model with daily data. You can see to output below:
/> summary(regression_resn)
Time series regression with "ts" data:
Start = -150, End = -26
Call:
dynlm(formula = ror_resn ~ ror_spi_resn)
Residuals:
M
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