I have multiple financial time series data (FX-rates, commodity prices) that have been recorded daily (without weekends) for the past six years and want to analyze their effect/influence on the stock price of a certain company.
I have tried to do this via ARIMA/ARMAX but somehow this does not lead to any plausible result (especially the in-sample forecasts generated by these models are rather poor).
Someone has given me the hint that maybe GARCH is a better method of modeling the dependencies of the above mentioned variables. I am very new to econometrics and do not have a mathematical background. Therefore I am looking for a simple explanation on how to come up with such a multivariate GARCH model (most preferably in Gretl). I would need some sort of manual/tutorial that (1) avoids all the math that underlies GARCH as much as possible and (2) describes the process of choosing the different parameters ($p$,$q$), the necessary independent variables that need to be included in the model, etc., step by step (something like this ARIMA manual but for GARCH). So far I have only found very sophisticated scientific papers that were far too mathematically for me to grasp...
I would very much appreciate any help - or if you feel I am totally off track, I also welcome any kind of corrections!