calibration of Garch models to historical data
This might be more of a question for R-SIG-Finance and followup should probably be there, but you might get a start with the rugarch package. Michael
On Wed, May 2, 2012 at 4:13 AM, Ivette <iva_mihaylova at mail.ru> wrote:
I have done the usual estimation of GARCH models, applied to my historical dataset (commodities futures) with a maximum likelihood function and selected the best model on the basis of information criteria such as Akaike and Bayes. Can somebody explain me please the calibration scheme for a GARCH model? I was not able to find a paper, dealing with exactly this algorithm for my case. I only understood that I have to compare the performance of the best GARCH model (from the estimation step), fitted to my historical dataset and a GARCH simulation (let's abbreviate this Squared Error difference to "E2"). However, it is not clear to me: - with what parameters' values to start this simulation, - how many times it is normal to perform it, and - what to compare via E2 (maximum likelihood values, or parameter values) - how to construct&assess E2 for the GARCH case. Thank you in advance for your suggestions. Ivette -- View this message in context: http://r.789695.n4.nabble.com/calibration-of-Garch-models-to-historical-data-tp4602606.html Sent from the R help mailing list archive at Nabble.com.
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