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. > > > > -- > 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). > > _______________________________________________ > [email protected] (mailto:[email protected]) mailing list > https://stat.ethz.ch/mailman/listinfo/r-sig-finance > -- Subscriber-posting only. If you want to post, subscribe first. > -- Also note that this is not the r-help list where general R questions > should go. > > [[alternative HTML version deleted]]
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