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I am doing a project for my class Financial Time Series in which I am trying to forecast my portfolio log returns using a GARCH fit. I am having a bit of trouble determining the best way to fit this model, and which order model is the best fit. I have tried everything from garchM to rugarch. So far, I have gathered that the best way to determine which order is adequate, is by comparing AICs for different ordered models. If someone could please get back to me that would be great! Thanks, Jeff W

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  • $\begingroup$ I'm pretty sure you can output the likelihood on rugarch, which means it's not much additional work to get the AIC. Then just write a function to compare the fits. $\endgroup$ – John Dec 11 '14 at 19:36
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Did you try rmgarch package of R ?

http://cran.r-project.org/web/packages/rmgarch/index.html

http://unstarched.net/r-examples/rmgarch/mgarch-comparison-using-the-hong-li-misspecification-test/

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    $\begingroup$ Don't forget rugarch by the same author $\endgroup$ – Kyle Balkissoon Jan 14 '15 at 4:03
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I would suggest you to forecast the series using different models and to determine which one is the best accordingly loss functions such as RMSE, MAPE.. or using the Mincer-Zarnowitz regression . You could also compare one-step forecast versus dynamic forecast. Another way is to compute VaR and observe the model having the lowest failure rate. AIC/BIC criteria are useful only to select the appropriate number of AR/MA/ARCH/GARCH terms.

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