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

[Eric Zivot]

For simple SV models (e.g. log normal ar(1)), the model can be written in
state space form and the the Kalman filter may be used to forecast the
latent volatility. See Harvey, Ruiz and Shephard's paper in ReStud for
details. However, the Kalman filter is only the best linear forecast. In
general, the SV models are non-linear and non-gaussian state space models
and the optimal forecasting algorithms are given by the particle filter. I
have a short paper that describes how to do this on my webpage

 http://faculty.washington.edu/ezivot/research/Creal_Gu_Zivot_2007.pdf

-----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 Guy Yollin
Sent: Thursday, February 28, 2008 4:30 PM
To: Michael; r-sig-finance at stat.math.ethz.ch
Subject: Re: [R-SIG-Finance] EMM: how to make forecast using EMM methods?

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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