Hi Ren?,
For quad prog algo, look for solve.QP from quadprog package in R. It
should allow you to do what you want (if I understood well). Please
note I am not too familiar with the PerformanceAnalytics package & its
capabilities (but suspect its style analysis function is a wrapper for
solve.QP). Building a rolling window analysis is quite trivial from
there.
On another note, using 1 month data is probably too small to have
stable results (depending on how many factors in your style analysis -
personal rule of thumb is 10 times the number of factors gives you a
benchmark of nbr of data points needed) , I would look to include more
returns in your linear regression. One could look into applying some
sort of weighting to your regression to improve forecasting power
(e.g. exponential weighting). I guess you are also looking into the
residuals for autocorrelation & heteroskedasticity which will impact
the hypothesis testing of your betas/coefficients.
& Finally, always best to make a question as concise as possible,
include a piece of reproducible code of what you are trying to do &
some system information (what R version, what OS) ... that often makes
easier for list member to help & follow (more or less) the posting guide.
HTH
Julien
On Mar 16, 2010, at 12:04 AM, Ren? Naarmann wrote:
Hi R-users,
it is the first time for me writing to this group. I would be
grateful if somebody could help me to find
a solution to my problem.
I am working on my final thesis and I would like to analyse the
impact of return frequency
using return-based style analysis. Specifically I would like to
calculate attributable returns. An attributalbe return
is the difference between the realised return of a fund and the
forecast from using the estimated style coefficients
multiplied by the respective indexseries.
I am using the implemented functions for style analysis in the
PerformanceAnalytics Package.
I would like to use the quadratic programming algorithm just for each
day in a specific month, i.e.
use the daily returns from january to calculate the style weigths.
This should be done month by month.
In the next step the calculation should include two months of data
and calculate the style weigths month by month.
So far I tried to usw apply.month in combination with style.fit. This
returns the same results for each month.
In the next step I tried to use some code out of chart.RollingStyle.
I change it for my purpose and
receive the style weights in a rolling calculation and could enable
the by option. So I get styleweights
over a specific width and could shift the calculation by a specified
block, i.e. calculate styleweigths
for 20 days shifting this calculation by the next 20 days. What I
would like to have is a shifting by month
to use the last month realised daily returns to forecast the style
weigths for the next month.
Has somebody an idea how to handle this problem?
Thank you in advance
Ren? Naarmann
--
E-Mail: rene.naarmann at mnet-online.de