[R-SIG-Finance] Fw: Value-at-Risk
Murali.MENON at fortisinvestments.com
Murali.MENON at fortisinvestments.com
Wed Jul 1 15:41:31 CEST 2009
Regarding the use of EVT-based VaR, I think it is dependent on the asset
class. In exchange rates, e.g., you may generally be served well with
EVT for the major currencies, but might do quite badly with emerging
market currencies. Likewise, in the credit markets, EVT may not do too
well. The assumption of a constant tail index results in misleading VaR
at the extreme tails, especially when there are several regime shifts.
Take a look at the paper "Testing for Multiple Regimes in the Tail
Behavior of Emerging Currency Returns" by B. Candelon and S. Straetmans,
LIFE Working Paper 03-035.
From: r-sig-finance-bounces at stat.math.ethz.ch
[mailto:r-sig-finance-bounces at stat.math.ethz.ch] On Behalf Of Debashis
Sent: 01 July 2009 14:25
To: Wei-han Liu
Cc: R-SIG-Finance at stat.math.ethz.ch
Subject: Re: [R-SIG-Finance] Fw: Value-at-Risk
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
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.
On 01/07/2009, Wei-han Liu <weihanliu2002 at 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?
> ----- Forwarded Message ----
> From: Robert Iquiapaza <rbali at ufmg.br>
> To: Wei-han Liu <weihanliu2002 at yahoo.com>; "
> r-sig-finance at stat.math.ethz.ch" <R-SIG-Finance at 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
> Sent: Tuesday, June 30, 2009 12:16 PM
> To: <R-SIG-Finance at 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
> > Wei-han Liu
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