FRANÇOIS-SERGE LHABITAN T 209Rule 8: Use a model for what it is made for
Most models were initially created for a specific purpose. Things may start
breaking down when a model is used outside its range of usefulness, or is
not appropriate for the intended purpose. For instance, a good model for
value at risk (VaR) will not necessarily be a good pricing model. The reason
is that VaR estimates focus only on price variations, but not on price levels.
Pricing errors are therefore not translated in the VaR. For the same reasons, a
good pricing model is not necessarily a good hedging model, and vice versa.
For example, using a stochastic or a deterministic volatility does not make
a huge difference as far as the pricing is concerned if one gets the average
volatility right. It makes a big difference as far as hedging is concerned.
Rule 9: Stress test your models
The G30 states that dealers should regularly perform simulations to deter-
mine how their portfolio would perform under stress conditions. This is
often implemented through scenario analysis, which is appealing for its
simplicity and wide applicability. Unfortunately, most institutions tend to
focus solely on extreme market events such as the October 1987 crash. They
neglect to test the impact of violations of the model hypothesis, and how
sensitive the model’s answers are to its assumptions. A small change in one
parameter may result in dramatic changes in the model output, while a large
change in another parameter may not necessarily change things at all.
Because there is no standard way of carrying out stress model risk testing
and no standard set of scenarios to be considered, the danger is that one does
not really suspect a model until something dramatic happens. To borrow a
metaphor from a well-known movie, the threat of a North Atlantic iceberg
was just a theory on 14 April 1912 – until the Titanic hit one. This is why the
process should also depend on the qualitative judgement and experience of
the model builder.
Rule 10: Beware of exotics!
By definition, exotic derivatives are highly subject to model risk. Firstly,
exotic derivatives are not traded on liquid markets, but over the counter.
Prices are therefore not the result of the equilibrium between supply and
demand with numerous arbitrageurs waiting to capture any mispricing,
but are rather supply driven. Secondly, exotic derivatives are often sensi-
tive to some exotic parameters that cannot be hedged, are embedded into
the model assumption, or are themselves linked to the difficulty of manag-
ing the risk. For instance, yield curve options pose vega spread volatility