Be aware of operational risk consequences

One should also be aware of possible operational risk consequences on model risk.
Several banks use very sophisticated market or credit risk models, but they do not
protect their systems from an incorrect data input for some ‘unobservable’ parameters,
such as volatility or correlation. For instance, in the NatWest case, the major
problem was that the bank was using a single volatility number for all GBP/DEM
options, whatever the exercise price and maturity. This wiped off the smile effect,
which was very important for out-of-the-money deals.
The problem can also happen with multiple yield curve products. In 1970s,
Merrill Lynch had to book a US$70 million loss because it underpriced the interest
component and overpriced the principal component of a 30-year strip issue.14 The
problem was simply that the par-yield curve used to price the components was
different from the annuity and the zero yield curves.
Many model risk problems also occur in the calibration of the model in use. This
is particularly true for interest rate derivatives, where complex models fail to capture
deeply out-of-the money behaviour.
To reduce the impact of operational matters on model risk, and particularly the
problem of inaccurate data, it is essential to implement as much as possible a central
automatic capture of data, with a regular data validation by mid-office, in agreement
with traders and dealers.