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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!

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  • $\begingroup$ You may start off by checking out the existing threads about order selection for GARCH models (although most of them consider univariate cases), perhaps this, this, this, this, and links therein. $\endgroup$
    – Richard Hardy
    Commented Jan 7, 2016 at 12:36
  • $\begingroup$ And here is a link to an R package that does automated model selection (both for conditional mean and conditional variance) given the regressors. It builds on reputable work of Jurgen Doornik ("Autometrics") and colleagues. $\endgroup$
    – Richard Hardy
    Commented Jan 7, 2016 at 13:39
  • $\begingroup$ thank you Richard for your quick and very helpful answer! Actually I have never used R really, is there any such solution (as "gets) that works in GRETL or even as add in excel or some other statistical software? Also would there be a possibility of contacting you and showing you some of my data? Thank you! $\endgroup$
    – Jonathan Rhein
    Commented Jan 7, 2016 at 15:46
  • $\begingroup$ Sorry, I am not ready to get my hands dirty now. I am unfamiliar with GRETL, so I have nothing to offer there; I also do not work with Excel's add ins. Also, I forgot to mention earlier: do not expect good fit for financial returns data (if you achieve that, you might have overfit) and watch out for spurious regressions if you use levels rather than returns. $\endgroup$
    – Richard Hardy
    Commented Jan 7, 2016 at 16:24
  • $\begingroup$ I checked out Gretl's manual and was not able to find multivariate conditional variance models; I only found univariate ARCH and GARCH (see p. 217-230). Should I look somewhere else, or can I conclude that one cannot fit multivariate conditional variance models in Gretl? (If so, your question becomes irrelevant as long as you insist on using Gretl.) $\endgroup$
    – Richard Hardy
    Commented Jan 13, 2016 at 19:29

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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.)

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  • $\begingroup$ Just to update the information a little: Since April 2017 there has been a function package for gretl called MGARCH that "handles unrestricted BEKK models (with analytical derivatives)". (I am not the author.) $\endgroup$
    – Sven S.
    Commented Nov 21, 2018 at 22:12

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