ARIMA & Conditional Heteroskedasticity How to deal with conditional heteroskedasticity in ARIMA model?
ARCH test on ARIMA model indicates the presence of conditional heteroskedasticity and ARIMA forecasts are therefore incorrect. 
Is there any way to fix it apart from using GARCH model ?
 A: Remedies often have side effects like suicide when an aspirin is all that is needed to eliminate a headache. Pulse outliers and/or seasonal pulses (untreated) can often incorrectly suggest " unwarranted complicated remedies" like power transforms , weighted least squares and arch/garch solutions. Keep the model as simple ( but not too simple !) in order to efficiently/correctly segment signal and noise. I am not arguing for an under-specified solution/remedy just one that is minimally sufficient. In my approach there are many solutions !
If you would like please post your data and I can be more specific as it relates to your data.
In response to the OP's comment 
heteroskedasticity is a symptom ..the  possible causes are multiple .. only your data knows for sure .. post your data and I will suggest a minimally sufficient "transformation" that results in a model error variance that is constant over time . That transformation could be as simple as adding a level shift , a seasonal pulse , a pulse or a local time trend.... OR incorporating weights to deal with an error variance that changes deterministically over time  OR a change in parameters over time  OR a dependence of the error variance and the level of the series over time OR an error variance that changes stochastically over time OR even an augmentation to your proposed arima model. Please post your model and parameters along with your data..
Many software packages often deal presumptively with complexity ... sometimes too complex ... where less intrusive solutions are superior due to their unstated software limitations ...
