Hi Guy, Thanks for your help! Yes, we have the coefficient estimated using EMM. And we followed those papers.
Just want to check my understanding about your suggestion: Do you mean that after we obtain the estimated coefficients, we run one simulation to obtain the whole sequence of latent variable (the volatility time series, from time 0 to time t+1), where time t is today, and t+1 is tomorrow(one step forecast); And that's one simulation. And we run such simulation for N times, let's say N=10000, and obtain 10000 such volatility time series, each ending at time t+1, and then we take average of the 10000 data points at t+1, the average will be the mean-forecast of the volatility tomorrow(i.e. that's the one step forecast that we want)... Am I right in doing these procedures? Thanks On Thu, Feb 28, 2008 at 4:30 PM, Guy Yollin <[EMAIL PROTECTED]> wrote: > Michael, > > If I understand correctly, you've used some EMM algorithms to estimate > the parameters of a stochastic volatility model. > > If this is the case you should now be able to use Monte Carlo methods to > generate forecasts from your model. > > That is, you will generate random variables (according to the > specifications of your model), feed them into your model and hence > simulate your stochastic volatility process. > > Note sure what references you have been using but perhaps these would be > helpful: > > Gallant, Hsieh and Tauchen (1997). "Estimation of stochastic volatility > models with diagnostics", Journal of Econometrics, 81, 159-192. > > Andersen, T.G. H.-J. Chung, and B.E. Sorensen (1999). "Efficient Method > of Moments Estimation of a Stochastic Volatility Model: A Monte Carlo > Study," Journal of Econometrics, 91, 61-87. > > Best, > > -- G > > > > > -----Original Message----- > From: [EMAIL PROTECTED] > > [mailto:[EMAIL PROTECTED] On Behalf Of Michael > Sent: Thursday, February 28, 2008 12:56 PM > To: [EMAIL PROTECTED]; r-help > > > Subject: [R-SIG-Finance] EMM: how to make forecast using EMM methods? > > Hi all, > > We followed some books and sample codes and did some EMM estimation, > only to find it won't be able to generate forecast. > > This is because in the stochastic volatility models we are estimating, > the volatilities are latent variables, and we want to forecast 1-step > ahead or h-step ahead volatilities. > > So it is nice to have the system estimated, but we couldn't get it to > forecast at all. > > There is a "Reprojection" Method described in the original EMM paper, > but let's say we reproject to a GARCH(1,1) model, then only the > GARCH(1, 1) parameters are significant, which basically means we > degrade the SV model into a GARCH model. There is no way to do the > forecast... > > Could anybody give some pointers? > > Thanks! > > > > _______________________________________________ > [EMAIL PROTECTED] mailing list > https://stat.ethz.ch/mailman/listinfo/r-sig-finance > -- Subscriber-posting only. > -- If you want to post, subscribe first. > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
