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There are
two ways of looking at VaR results. And this really depends on
your approach towards dealing with risk, i.e. your management
"style". Defensive or aggressive ? Tactical or Strategic ?
You can look at the Same 1 Mio
USD VaR result in two ways:
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- You have 95 % chances of
losing AT MOST
1 Mio USD.
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This also
means that 19 out of 20 business days (i.e. 1 Calendar Month). you
should
NOT loose more than 1 Mio USD.
This is the
"Going concern"
approach: The Investor or Risk Avoider looks at
his daily business and how risk might impact negatively his bottom line.
He places himself under the probability distribution of returns (the right
hand bell shape of the picture above). This tactical approach is
typically what a risk officer in the corporate world should be
doing.
The 1 Mio USD
example is our WORST estimate
of our 19 days in the month.
This also implies there is ONE day where we will
loose MORE than 1 Mio dollars.
You can also look at
the same result the other way round:
This
is the Extreme Event
perspective, typical of the speculator or Risk Taker.
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- You have 5% chances of loosing AT
LEAST
1 Mio USD. |
This means that one day out of 20 (one
working day in the month), you will be loosing
more than 1 Mio USD.
Knowing the
shortcoming of normal distribution assumptions (see six sigma
events), we are placing ourselves under the left hand side of
the probability distribution of returns (see picture above), which
is also commonly known as the TAIL.
A Risk taker is really interested in
this side of the coin. What he really wants to know is the loss incurred
during these "5% percent of the time" events.
If he can anticipate reasonably well this loss, he can avoid
forced liquidation of his positions or even worse, bankruptcy. Yes,.. but is this
our best estimate!
Yes ! the biggest flaw and conversely strength of VaR lies
in Normal
distribution
assumptions.
There has been a lot of debate regarding Normality shortcomings.
Newcomers to Risk often assume this tends to invalidates results. Well,
first of all, Normal distribution assumptions hold quite well when:
-looking at risk from the
perspective of a going concern.
-the portfolio is well distributed across asset classes
and
market data
-the horizon is short (and the data is sampled daily).
Best Practice Risk Management is actually much more important than
the technical intricacies nested in the models used to compute VaR.
Proper Risk Management should ALWAYS be Complemented with Stress Tests and Marginal
Sensitivity Measures to
identify and pinpoint so called "hot-spots".
Normality assumptions are obviously
not valid when assessing extreme events,
But in most cases this is not important at all. What we need is a
clear view of our risks and we can get that by following a
disciplined approach that defines a combined methodology
policy (i.e. Parametric for linear, well balanced short term view,
Monte-Carlo for complex Derivative Portfolios ? Historical for
specific spread risks and a series of scenarios to highlight hot
spots).
By implementing a combined approach you can build upon the strength
of each model and forget their shortcomings!
Relative
VaR
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