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Preparing data for Superior predictive ability (SPA) test

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