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

[Thomas Etheber]

Dear Rene,

why are you trying to circumvent the already implemented wrapper in 
style.QPfit?
If I got your problem right, you could just slice your data to rolling 
estimation windows and then forward it to style.QPfit. Did you already 
have a look at rollapply, I didn't test it but I think this should work 
for your purpose. In this case you will have to store the coefficients 
in each run, so that you are able to calculate the unsystematic returns, 
e.g. in a loop.

Hth
Thomas


René Naarmann schrieb:
> 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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>>>
>>
>>
>>
>