Dear Wei-han,

I believe EVT based VaR would provide a better solution specially
in stressed situation like the present one, modeling the extremal behaviour
in the tail. I used Peaks Over Threshold (POT) based VaR method in my
doctoral dissertation.

Back testing and comparing the new method to existing ones on real financial
events show that this POT based VaR method provides a rather realistic model
for the extremal behavior of financial processes, enabling a precise
estimation of risk measures. Through the GPD , the model provides a way of
estimating the tail behaviour of the random variables without knowledge of
the true distribution and as such it is a good candidate for Vale at Risk
computation.

Most common at this moment is the tail-fitting approach based on the second
theorem in extreme value theory (Theorem II Pickands(1975), Balkema and de
Haan(1974)). In general this conforms to the first theorem in extreme value
theory (Theorem I Fisher and Tippett(1928), and Gnedenko (1943)).The
difference between the two theorems is due to the nature of the data
generation.

 For theorem I the data are generated in full range, while in theorem II
data is only generated when it surpasses a certain threshold (POT's models
or Peak Over Threshold). The POT approach has been developed largely in the
insurance business, where only losses (pay outs) above a certain threshold
are accessible to the insurance company.

 Kind Regards,
Debashis

On 01/07/2009, Wei-han Liu <weihanliu2002@yahoo.com> wrote:
>
> Thanks a lot, Robert.
>
> I know GARCH models has its forecasting capacity as the reference you
> shared indicates.
>
> I wonder if the Value-at-Risk estimated by extreme value theory can also be
> used for forecasting purpose. Is there some theory background in this
> regard?
>
> Wei-han
>
>
>
> ----- Forwarded Message ----
> From: Robert Iquiapaza <rbali@ufmg.br>
> To: Wei-han Liu <weihanliu2002@yahoo.com>; "
> r-sig-finance@stat.math.ethz.ch" <R-SIG-Finance@stat.math.ethz.ch>
> Sent: Wednesday, July 1, 2009 6:37:21 PM
> Subject: Re: [R-SIG-Finance] Value-at-Risk
>
> See for example "Accurate value-at-risk forecasting based on the
> normal-GARCH model" by C Hartz, S Mittnik, M Paolella - Computational
> Statistics and Data Analysis, 2006
>
> best
>
> --------------------------------------------------
>
> Sent: Tuesday, June 30, 2009 12:16 PM
> To: <R-SIG-Finance@stat.math.ethz.ch>
> Subject: [R-SIG-Finance] Value-at-Risk
>
> > Dear R-users:
> >
> > Several questions please on Value-at-Risk.
> >
> > Is Value-at-Risk designed for forecasting purpose?
> >
> > I wonder if Value-at-Risk estimated by in-sample data can be used for
> out-of-sample forecasting?
> >
> > If in-sample Value-at-Risk is estimated by several methods, is it
> appropriate to do the model comparisons based on out--of-sample performance?
> >
> > Wei-han Liu
>
>
>
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>
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