208 MODEL RISK AND FINANCIAL DERIVATIVESunderstanding of risks, the creation of new financial products, and, there-
fore, the need for additional models. As an illustration, the elegance of
the Black and Scholes model is its rationality and logic. The model was
not successful because prices of financial assets were actually log-normally
distributed (which they may or may not be), but because the formula was
easy to apply and understand, it arose as a valid first order approximation in
a much wider class of models. The later Black and Scholes stochastic exten-
sions (for example, with stochastic interest rates and/or stochastic volatility)
were never as successful as the original model because they lost most of the
qualities of their ancestor. As a rule, users should always understand the
ideas behind a model and be comfortable with the model results. Treating a
model as a black box is definitely the wrong approach.
Rule 7: Verify your data
A few years ago, the lack of reliable financial data was a major problem. It is
still the case in a few areas (for example, the modeling of exotic derivatives
or of credit risk). However, most of the time, we are rather awash with data.
The key is turning this data into knowledge. Information should no longer
be represented by data, but by data verified and organized in a meaning-
ful way. The quality of a model’s results depends heavily on the quality of
its data feed. Garbage in, garbage out (GIGO) is the law, and data which
is faulty to start with is likely to produce faulty conclusions after process-
ing, and further, may ruin the benefit of sophisticated analytical models.
Ensuring the integrity and accuracy of data feeds in models should there-
fore be key, even though it may require considerable effort and time. This
implies checking both the series of data against errors, but also the seman-
tics of the feed. Should the fair value be the price at which the firm could
incrementally unwind the position, or the price at which they could sell
the entire book, or the price above which they start to lose clients’ inter-
est? These questions need to be addressed at the beginning of the modeling
process.
As an illustration, in the 1970s, Merrill Lynch had to book a US$70 mil-
lion loss because it underpriced the interest component and overpriced the
principal component of a 30-year strip issue.^9 The market identified the
mis-pricing and only purchased the interest component. The problem was
simply that the par-yield curve Merrill used to price both components was
different from the annuity and the zero-yield curves that should have been
used for each component. Oops! Wrong feed ... As a rule, one should also
beware of multiple data sources and non-synchronous data feeds (for exam-
ple, stock indices and foreign exchange for daily close values) should also be
reduced to a minimum, as they can lead to wrong pricing or create artificial
arbitrage opportunities.