[R-SIG-Finance] Preparing data for Superior predictive ability (SPA) test

[Brian G. Peterson]

I asked you a reproducible example, and instead, you're asking the list 
to do a bunch of work to replicate your code for you.

Please respect the time of the list members, and provide a few lines of 
code, so that we can *run* your example code and then *help you* with 
your problem.

Brian

On 11/28/2014 09:44 PM, Karthik Raju wrote:
> Hello Brain,
>
> Thank you for your response.
>
> I will explain my work with an example.
>
> - I have logarithmic returns computed from daily closing values of a
> stock market index for a period of 500 days.
>
> - The logarithmic returns are used in the "rugarch" package to fit the
> following models,
> Model-1 - GARCH (1,1)
> Model-2 - GJR-GARCH (1,1)
> Model-3 - EGARCH (1,1)
> Model-4 - IGARCH (1,1)
> Model-5 - APARCH (1,1)
>
> - Then, using "rugarch", forecasted one step ahead volatility with each
> of the above model parameters for an out-of-sample period of 100 days.
>
> - The forecasted 100 days "sigma" values of each model is extracted from
> R to a worksheet and used against true volatility proxy to compute the
> loss functions.
>
> - The structure of loss function data for each model is an univariate
> series for the forecasted period (100 days).
>
> - For example, the structure of Mean Square Error (MSE) loss function
> looks like,
>
> Forecasts    MSE_GARCH   MSE_GJR   . .  MSE_APARCH
> Day 1        0.008548    0.005509  . .  0.00412
> Day 2        0.008655    0.005498  . .  0.00414
> Day 3        0.008759    0.005488  . .  0.004156
> Day 4        0.008861    0.005478  . .  0.004169
> .        .        .          . .  .
> .        .        .          . .  .
> .        .        .          . .  .
> Day 99        0.008759    0.005488  . .  0.004156
> Day 100        0.008861    0.005478  . .  0.004169
>
> - Similarly, the loss functions like Mean Absolute Error, Mean Absolute
> Percentage Error etc. are in the same structure, which are then imported
> into R.
>
> - Now, by using each of this loss function data series,  one at a time,
> in the "ttrTests" package, I want to know how to compute the SPA test
> for the following combinations?
>
> Benchmark model GARCH (1,1)      Vs rest of 4 models
> Benchmark model GJR GARCH (1,1)      Vs rest of 4 models
> Benchmark model EGARCH (1,1)      Vs rest of 4 models
> Benchmark model IGARCH (1,1)      Vs rest of 4 models
> Benchmark model APARCH (1,1)      Vs rest of 4 models
>
> Best,
> Karthik
>
>> Please follow the posting guide
>>
>> http://www.r-project.org/posting-guide.html
>>
>> and provide a reproducible example.  For instance, you could repeat your
>> 'n' samples using some of the test data from rugarch.
>>
>> It is very difficult for others to help you if we don't know the format
>> your data is in *now*.
>>
>> Regards,
>>
>> Brian
>>> On 11/27/2014 11:24 PM, Karthik Raju wrote:
>>>
>>> Hello,
>>>
>>> Can anyone please let me know how to prepare data to compute Superior
>>> predictive ability (SPA) test in R?
>>>
>>> I am working on forecasting volatility in stock markets, the context is,
>>>
>>> 1) I used "rugarch" package to forecast the volatility using "n"
>>> autoregressive models.
>>>
>>> 3) Extracted "sigma" values from the forecasts of those "n" models and
>>> used it in a worksheet against realized volatility to calculate daily
>>> loss functions for each model, say, MSE, MAE, RMSE etc.
>>>
>>> 3) I want to compute the SPA test statistics using "ttrTests" package
>>> and find the SPA test p-values for a benchmark model against "n"
>>> alternatives. How to prepare data for this purpose?
>>>
>>> Thanks,
>>> Karthik
>
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-- 
Brian G. Peterson
http://braverock.com/brian/
Ph: 773-459-4973
IM: bgpbraverock