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

[Guy Yollin]

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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