I'm an analyst in financial and insurance fields and whenever I try to fit volatility models I obtain awful results: residuals are often non-stationary (in the unit root sense) and heteroskedastic (so the model doesn't explain volatility).

Do ARCH/GARCH models work with other kind of data, maybe?

Edited on 17/04/2015 15:07 to clarify some points.

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#### Best Answer

My experiences with programming/implementing and testing ARCH/GARCH procedures have led me to the conclusion that they must be useful somewhere and someplace but I haven't seen it. Gaussian violations such as unusual values/level shifts/seasonal pulses and local time trends should be used initially to deal with changes in volatility/error variance as they have less serious side effects. After any of these adjustments care might be taken to validate that model parameters are constant over time . Furthermore error variance may not be constant but simpler/less intrusive remedies like Box-Cox and detecting deterministic break points in error variance ala Tsay are much more useful and less destructive. Finally if none of these procedures work then my last gasp would be to throw ARCH/GARCH at the data and then add a ton of holy water. I firmly agree with your findings and conclude that these are methods looking for data or just dissertation topics flying in the wind.

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