[R-SIG-Finance] style.fit by month

Tue Mar 16 16:06:34 CET 2010

Hi all,

thank you Julien for your thougts. I working with R 2.10.0 on Windows XP.
I remembered the function applySeries and fapply respectively. I tried 
to calculate
a variance covariance matrix over different periods. When this works I 
could go on with solve.QP which needs the Dmat.

Here is an example:
require(timeSeries)
btime <- timeSequence(from="1999-01-01", to="1999-03-31", by = "month")
etime <- timeLastDayInMonth(btime[-1])
btime <- btime[-length(btime)]
data1 <- rnorm(1:90)
data2 <- matrix(rnorm(270), ncol = 3)
colnames(data2) <- LETTERS[1:3]
datats <- timeSequence(from="1999-01-01", to="1999-03-31", by = "day")
S <- timeSeries(data2, datats)
applySeries(S, btime, etime, FUN = function(x) cov(x, 
use="pairwise.complete.obs"))
cov(window(S, start="1999-01-01", end="1999-02-28"))

The result is a matrix which contains the varcov matrices for 2 
subperiods which is fine,
but when I am using my real Data I receive this Message:
Fehler in midnightStandard2(charvec, format) :  'charvec' has non-NA 
entries of different number of characters
I don't understand this error message, can anyone help?

I read my datafile from a spreadsheet via readSeries(), the head looks 
like this:
                    R2G          R2V           R1G           R1V    
MSCIWexUS
1999-01-04 -0.006477506  0.004066846 -0.0026417776 -0.0009521722  
0.018939565
1999-01-05  0.001725935  0.002339151  0.0128278433  0.0116685255  
0.006108505
1999-01-06  0.018860732  0.007514636  0.0211913231  0.0215123444  
0.019311270
1999-01-07  0.005040827 -0.004752986 -0.0028397673  0.0013254882 
-0.005757679
1999-01-08  0.011525972  0.003911511 -0.0006109632  0.0101291838  
0.001059926
1999-01-11  0.013880501 -0.005775207 -0.0039712968 -0.0108176871 
-0.011788948

Thanks in advance
René

julien cuisinier schrieb:
> Hi René,
>
>
>
> 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, René 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
>>
>> René Naarmann
>>
>> -- 
>> E-Mail: rene.naarmann at mnet-online.de
>>
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>
>
>

-- 
René Naarmann
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