Hi Alexios After having (somewhat) digested the link you sent me to the seasonal variance models in your package, I'm under the impression that the conditional distribution is still restricted to being normally distributed (with varying variance)? I was hoping to use a skewed-normal as the conditional distribution at certain times of day.
Another thing is that at the bottom of the page you say: " Another possible direction for expansion would be to treat the diurnal effect separately for each day of the week." This is an interesting idea, and is something I thought about, but was a little uneasy about the ideas that popped into my head about how to go about this. For example, one simple approach might be (at least in my case) to transform each hour of the diurnal cycle separately to try and match the conditional distributions as closely as possible. However, this seems like it would leave to an overly complicated model. Cheers -- View this message in context: http://r.789695.n4.nabble.com/Changing-seasonal-conditional-distribution-in-a-fGarch-model-tp4690850p4690858.html Sent from the Rmetrics mailing list archive at Nabble.com. _______________________________________________ R-SIG-Finance@r-project.org 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.