210 MODEL RISK AND FINANCIAL DERIVATIVESissues; Bermudian options create modeling problems due to their hybrid
nature between American and European; and ratchet options pose difficul-
ties associated with the existence of a volatility smile slope. None of these
variables are directly hedgeable. And finally, models may produce similar
plain vanilla option prices (and therefore fit to the market data), yet give
markedly different prices of exotic options. This is documented for instance
in Hirsa, Courtadon and Madan (2002).
Rule 11: Beware of correlations!
Correlations are found almost everywhere in finance, from portfolio con-
struction to option pricing and hedging. As soon as there is more than
one random parameter to be considered, correlations have a role to play.
Unfortunately, correlations are among the most unstable parameters in real
life, particularly during periods of heightened volatility. Risk managers
often consider the possible effects of high return volatilities, but fail to
account for the higher correlations between asset returns that would gener-
ally accompany the elevated volatility. One way to do so would be to employ
information from historical periods of high volatility in order to form esti-
mates of correlations conditional to a period of heightened volatility. These
conditional correlations could then be used to evaluate the distribution of
returns under a high volatility scenario. Put differently, the method used for
stress testing a portfolio must not exclude the empirical feature that periods
of high volatility are also likely to be periods of elevated correlation.
10.8 Conclusion
Acknowledging the rapid increase in sophistication of the financial commu-
nity and products in recent years, most banks and trading rooms have been
hiring people with the most up-to-date set of mathematical and quantita-
tive skills. This directly resulted in a profusion of complex models – math
engines that spit out risk and instruments valuations based on a flood of
market data – that corporations are relying on to steer them through volatile
markets.
Although most money is still made or lost because of market movements,
not because of modeling, institutions are increasingly aware that no matter
how advanced and refined financial models are, they are all subject to model
risk and they should all be extensively tested, validated and tempered with
judgment. However, intensive model-auditing, stress-testing and smart risk
managers are all necessary – but they aren’t enough. All the math geniuses
in the world don’t help if management either neglects to implement the
procedures necessary to produce accurate calculations of risk or ignores