I agree with Pat. no clear winner I think

A good reference paper to look at:
Forecasting Volatility in Financial Markets: A Review (revised edition) 
SER-HUANG POON 
Manchester Business School
CLIVE W.J. GRANGER 
University of California, San Diego - Department of Economics 
--------------------------------------------------------------------------------
September 18, 2002
 
available on: 
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=331800






Patrick Burns <patrick@burns-stat.com> 
Sent by: r-sig-finance-bounces@stat.math.ethz.ch
02/29/2008 09:56 AM

To
elton wang <ahala2000@yahoo.com>
cc
r-sig-finance@stat.math.ethz.ch
Subject
Re: [R-SIG-Finance] EMM: how to make forecast using EMM methods?






(This is not being sent to R-help.  It is considered
impolite to cross-post messages, especially on topics
that are purely financial.)

As always, the answer is, "That depends."

The key question is the time frame of the
prediction.  If the prediction is for a month or
more, then there's unlikely to be much advantage
in a fancy model.  If the time frame is a few
days, then something like a garch model will
vastly outperform a rolling window.  How much
a garch model would outperform an exponential
smooth depends on the smoothing parameter (an
exponential smooth is a degenerate form of a
garch model).  As far as I know, there is not a
clear winner between garch models and stochastic
volatility models, but with some evidence that garch
might be better.  Corrections to this impression are
certainly welcome.


Patrick Burns
patrick@burns-stat.com
+44 (0)20 8525 0696
http://www.burns-stat.com
(home of S Poetry and "A Guide for the Unwilling S User")

elton wang wrote:

>I've heard opinions that GARCH/SV volatility models
>are not better on forecasting than simple exponential
>moving average volatilities or even rolling window
>historical vol.
>Any practitioners mind comment?
>
>--- Michael <comtech.usa@gmail.com> wrote:
>
> 
>
>>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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>>
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