[R-SIG-Finance] EMM: how to make forecast using EMM methods?

[Michael]

Hi Guy and Elton,

Thanks for the replies.

However this is exactly the weird thing about EMM. It simulates the
latent variables when doing the estimating itself. So there is no
clear estimates of the latent variables itself. That's to say, I don't
have a "today's value" to work out the 1-step ahead forecast...

That's kind of strange...

Any thoughts?

On Fri, Feb 29, 2008 at 7:37 AM, elton wang <ahala2000 at yahoo.com> wrote:
> But I doubt this is not a one-step forecast.
>  For one-step cast, you only need start from today's
>  value and simulate one step ahead. you need to use the
>  orignal innovations as of today instead of simulating
>  from day 1.
>
>
>
>  --- Guy Yollin <guy.yollin at rotellacapital.com> wrote:
>
>  > Hi Michael,
>  >
>  > Yes, this is what I'm suggesting.  Bear in mind,
>  > your model estimation
>  > process should have also resulted in volatility
>  > estimates for t-1, t-2,
>  > etc.
>  >
>  > Your simulation will require one or more of these
>  > terms as input (in
>  > addition to the random innovations) since your
>  > stochastic volatility
>  > model will have lagged volatility terms.
>  >
>  > Good luck.
>  >
>  > -- G
>  >
>  >
>  > -----Original Message-----
>  > From: Michael [mailto:comtech.usa at gmail.com]
>  > Sent: Thursday, February 28, 2008 5:46 PM
>  > To: Guy Yollin; r-help;
>  > r-sig-finance at stat.math.ethz.ch
>  > Subject: Re: [R-SIG-Finance] EMM: how to make
>  > forecast using EMM
>  > methods?
>  >
>  > 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
>  > <guy.yollin at rotellacapital.com> 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: r-sig-finance-bounces at stat.math.ethz.ch
>  > >
>  > > [mailto:r-sig-finance-bounces at stat.math.ethz.ch]
>  > On Behalf Of Michael
>  > >  Sent: Thursday, February 28, 2008 12:56 PM
>  > >  To: r-sig-finance at stat.math.ethz.ch; 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!
>  > >
>  > >
>  > >
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