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I am trying to use ARIMA to forecast stock returns. The problem is that variance is time-varying but ARIMA assumes that it is constant. As I understand, GARCH is only used for forecasting volatility (conditional variance), but not the conditional mean in which I am interested in. Is there any way to combine GARCH and ARIMA models?
Is it possible (maybe with some model specifications) to account for volatility clustering and to improve return forecasts? Thanks a lot!
It is very common to build ARIMA-GARCH models, i.e. ones with an ARIMA specification for the conditional mean and GARCH for the conditional variance. You can see this from
Point forecasting stock returns is notoriously difficult, so I would not expect much success with that regardless of what model you choose. However, modelling the conditional variance with GARCH can be a good way for accounting for volatility clustering, and GARCH remains a popular class of models for that.
