garchfit vs estimate inconsistent results and speed

Hello I always used garchset and garchfit for work pourpose and since I switched to 2016a I can't use those functions anymore so I am stuck with 2013a. The new functions that substitute garchset and garchfit are ARIMA and estimate. I will provide the code I use to switch between old and new functions but the problem is that the new functions are way slower.

OLD ONE: garchset + garchfit

 % set the model ARMA(1,1) + GARCH(1,1) with errors distributed like a t-student with 6 DoF
 mdl = garchset('Distribution' , 'T' ,'DoF', 6,'P',1, 'Q', 1, 'R', 1, 'M', 1, 'VarianceModel', 'GARCH');
 % estimation of coefficients
 tic
 garchfit(mdl, r1)
 toc

this took 0.2 second

NEW METHOD: ARIMA + estimate

 % specify the model for the conditional variance as GARCH(1,1) with errors distributed like a t-student with 6 DoF
 varmdl = garch('GARCHLags', 1, 'ARCHLags', 1, 'Distribution',struct('Name','t','DoF',6))
 % specify the model for the mean as ARMA(1,1) with errors distributed like a t-student with 6 DoF
 mdl = arima('ARLags',1, 'MALags',1,'Distribution',struct('Name','t','DoF',6),'Variance', varmdl)

 % setting the same option for the fmincon function that were used by garchfit
 opt=optimoptions(@fmincon,'PhaseOneTotalScaling','off', ... 
    'NoStopIfFlatInfeas','off','Algorithm','active-set', 'UseParallel','never')
 % estimation of coefficients
 tic
 es = estimate(mdl, r1, 'Options', opt)%, 'Display', 'off');
 toc

this took about 3 seconds and despite I used the same dataset and the same options for fmincon the results were slightly different. So what's happening? It seems to me like the new functions are worse. I don't care much about the difference in estimated coefficients but the speed is more that 10 times SLOWER. I use this to get real time signals for trading but with large number of stocks in my portfolio it is unusable anymore. And I don't want to be stuck wit 2013a either. Could someone please help me understand what's happening here?

thank you

[Michael Duerk]

Hello,

unfortunately I am not able to provide a solution for you. We did ecounter the same problem tho. I tried different algorithms, however no luck there as well. It seems the ML-Estimator is more thorrow and complex which results in longer compilation time....!

Do you have any new thoughts or ideas concerning the problem?

 % Converting old GARCH functions to new Model Objects  
    % This example does not show how to reproduce equivalent results between the models, 
    % because, in general, the estimates between the two functionalities might differ.
    
    
    % Set the optimization algorithm as interior-point so that Q will not
    % be exactly zero

       options = optimoptions('fmincon','Algorithm','interior-point');
       
    % Specify the model shell
    
       model1 = garch('Offset',NaN,'GARCHLags', 1, 'ARCHLags', 1,...
               'Distribution','t');
    %Estimate the model surpress the Output 
    
       [Coeff,Errors,logL,info] = estimate(model1,Renditen,'Display','off','options',options);

Thank you!

Greetings