FRANÇOIS-SERGE LHABITAN T 207model risk source (the mark to model syndrome). If the benchmark model is
wrong, everything can go wrong. Institutions following this direction will be
particularly at risk if its traders (relying on the wrong model) are themselves
the unique providers of a given financial instrument on the market. Then,
the market prices coincide with the incorrect model prices which means that
large neutral positions could in fact generate important accumulated losses
when the situation is discovered.
Although not ideal, marking to model may be acceptable if all market
participants agree on a standard. However, there are fields with no consen-
sus on a particular model. Consider for instance fixed income securities and
interest rate modeling. Since the valuation of most assets relies on discount-
ing cash flows, interest rate modeling is a very important area of finance.
However, no definitive interest rate model has yet emerged.^8 This is good
news for those who wish to carry out research in this line, but it is also a
source of concern to investment banks and their regulators, as a mark to
model gain or loss is clearly meaningless.
Rule 6: Simple is beautiful
The development of modern financial theory has come to a stage in which
finance produces a rich source of challenging questions for a range of
mathematical disciplines, including the theory of stochastic processes and
stochastic differential equations, numerical analysis, the theory of optimiza-
tion, and statistics. Theoretical results and computational tools are used,
for instance, in the pricing of financial derivatives, for the development of
hedging strategies associated with these derivatives, and for the assessment
of risk in portfolios. Unfortunately, as the mathematics of finance reaches
higher levels, the level of common sense seems to drop. Rather than starting
with some idea, some concrete economic or physical or financial mecha-
nism, and then expressing it in mathematics, researchers increasingly just
write down an equation and try to solve it without any consideration of
the usefulness of the overall process or its applicability to the real world.
We believe this approach is clearly wrong: models should be based on con-
cepts, information and insight, not just on advanced mathematics. Although
mathematics is important to modeling, it should not be primary, but mostly
complementary. Most financial models users will be fast-thinking actors
in dynamic markets. Therefore, avoiding unnecessarily complicated mod-
els should be the rule. Whenever available, simple, intuitive and realistic
models should always be preferred to complex ones.
For the same reason, model-users should only move to a more complex
model or approach only when there’s a value in doing so. In a sense, the
science of modeling should be seen as an evolutionary process, a sort of
chicken-or-egg problem. Better models should in turn allow for a better