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-reversion… 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.  
>  
>  
>  
> --
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> Sent from the Rmetrics mailing list archive at Nabble.com (http://Nabble.com).
>  
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