Hi: check out the SDE package and the associated UseR book. There may be
relevant material in there.




On Sat, Jun 15, 2013 at 12:31 PM, Dominykas Grigonis <
[email protected]> wrote:

> You will need to write your own functions for likelihood. Then AIC and BIC
> are straight forward once you have log-likelihood. This will be a pretty
> tedious process as for GBM log-likelihood will be straight forward, for
> mean-reversionÂ… its in principal similar to arma, however never had to do
> it myself, don't think its easy. Anyways, I would recommend comparing
> simulated distributions rather than log-likelihoods.
>
>
> Kind regards,--
> Dominykas Grigonis
>
>
> On Saturday, 15 June 2013 at 17:24, ousbens wrote:
>
> > I would like to find out if a GBM (Geometric Brownian motion) process or
> a
> > mean reverting Ornstein-Uhlenbeck (OU) process fits better to a time
> series.
> >
> > To determine this I would like to calculate the AIC, BIC and Log
> Likelihood
> > values for the GBM and OU processes (and also for a simple Jump diffusion
> > process).
> >
> > How can this be done in R?
> >
> > Many thanks.
> >
> >
> >
> > --
> > View this message in context:
> http://r.789695.n4.nabble.com/How-to-calculate-AIC-and-BIC-for-GBM-and-OU-processes-in-R-tp4669607.html
> > Sent from the Rmetrics mailing list archive at Nabble.com (
> http://Nabble.com).
> >
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