Why risk models used by financial analysts are fundamentally flawed

A  letter I sent to the Financial Times, 2 March 2011:

John Kay's analysis of why the models used by financial analysts are
fundamentally flawed when it comes to predicting rare events ("Don't blame
luck when your models misfire" FT 1 March 2011) is correct but overly
pessimistic as he focuses only on 'traditional' statistical techniques
that rely on relevant historical data. These flawed methods cannot
accommodate even simple causal explanations that involve new risk
factors where previous data has not been accumulated. It is like trying to
predict what happens to the surface area of a balloon as you puff into it,
by relying only on data from puffs of this balloon. If, after each puff, you
measure the surface area and record it, and after say, the 23rd puff, you
create a statistical model showing how surface area increases with each
puff, you will then have a 'model' to predict what will happen after a
further 20, 50 or 100 puffs. None of these predictions will tell you that
the surface area will drop to zero as a result of the balloon bursting,
because your model does not incorporate the basic causal knowledge.
Fortunately, and in contrast to the article's dire conclusions, there are
formal modelling techniques that enable you to incorporate causal,
subjective judgements about previously unseen risks, and allow you to
predict rare events with some accuracy. We have been using such techniques
- causal Bayesian networks - successfully
in research and in practice for several years in real applications ranging
from transport accidents through to terrorist threats. We remain stunned
by the financial markets poor take-up of these methods as opposed to those
which have consistently proved not to work.