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!
Best Answer
I'm sorry to say that Richard's hunch is correct, multivariate GARCH models aren't in gretl yet, neither in the core nor in the "gig" (garch-in-gretl) add-on. (Such a feature can be added through a function package in gretl's scripting language hansl, leveraging the ML routines without having to do the coding in C, but so far nobody seems to have contributed that.)