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I'm trying to compute the standard ARMA(1,1)-GARCH(1,1) as shown in this answer for an entire index,just to store in a database to quickly lookup values for back testing purposes. There is just one problem that the optimization method used by rugarch doesn't always converge giving and yields the error. I'm using minute equity data.

failed to invert hessian

Is there an easy work around or evasive solution to guarantee that it will always converge?

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i've found that GARCH fails to converge often due to outliers. Try winsorizing the data at 98% and it should converge. If not try asymmetric GARCH. Best is probably to use a new outlier robust GARCH but I don't think rgarch has those yet. This might be the case with datastream data if the market is open on different days to the standard daily dates that Datastream uses, in which case there'll be a large number of zero return days, but I haven't really explored this hypothesis yet (but I suspect it given the type of data that fails to converge that I've played with). –  Jase Feb 9 '13 at 6:34
    
Have you tried several of the different optimization methods that rugarch offers, or just the default? –  Benjamin Kjellson Feb 9 '13 at 16:51
    
@BenjaminKjellson I've tried the solnp and the nlminb solvers not sure if there are more or which is the default –  pyCthon Feb 9 '13 at 19:29
    
@Jase I think your right in both cases, and that my problem is a large number of zero return days( since i'm using minute data) the problem occurs much less while using daily data –  pyCthon Feb 9 '13 at 19:31

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