One of the strengths of risk models is supposed to be their
ability to use a limited range of parameters to predict events.
However, the reliability of these mathematically-driven models has
been called into question by the subprime turmoil, and regulators
are now eager to rationalize models and to develop stronger risk
Risk models depend partly on financial research, which must
continually be updated to keep in step with the dynamics of
ever-changing markets. This constant, rapid evolution heightens the
challenge of developing reliable models.
The lack of data available on extreme events also makes it
difficult to validate models. Nicole El Karoui, a professor at
l'École Polytechnique in Paris, uses an example from the auto
industry to illustrate why models have proven insufficient during
periods of extreme market stress. When a car is built to drive at
70 miles per hour (mph) and is driven consistently at 110 mph, she
notes, breakdowns can occur.
Another example of a model failure connected to insufficient
extreme events data can be found in the aerospace industry. On June
4, 1996, Ariane 5, an unmanned rocket launched by the European
Space Agency, exploded roughly 40 seconds after lift-off.
Unfortunately, Ariane 5's guidance system was based on the model
developed for its predecessor (Ariane 4), and it therefore could
not cope with the large increase in velocity it experienced.
Engineers could not forecast this extreme event, even after 10
years of extensive engineering and reliability tests.
In finance, model calibration faces the bias variance dilemma when
forecasting the evolution of markets. Basically, this means that
overall behavior patterns cannot be captured because the data
needed to capture different patterns is insufficient.
Generally speaking, during the crisis, there has been too much
trust in risk models developed by external data providers, such as
rating agencies. But internal models also share part of the blame
for the credit crunch.
For example, Gary Gorton, a model consultant for American
International Group, expressed his full confidence in his firm's
credit-default-swap models and highlighted their independence from
external data during an AIG investor meeting in December 2007. His
confidence was not necessarily misplaced, but the models he cited
were not designed to manage collateral margins resulting from huge
drops in value for insured credit assets.
Ignoring Human Behavior
Financial laws are very different from the laws of physics
because they rely on human reactions, which are difficult to
predict. Humans react differently when faced with unexpected risks,
such as the terrorist attacks of September 2001. Such events could
result in panic and amplification of the risk event.
Whereas in physics, models are used to predict the future fairly
efficiently (e.g., weather forecasts), the forecasts of financial
models are inherently biased. Consequently, to make good decisions,
one must consider uncertainty and go beyond numbers.
There is no denying that computations are essential in any
decision-making process, but risk management could take some
pointers from fields such as surgery, which relies heavily on human
decision making. Lest we forget the power of this tool, we should
remember the example of the pilot who managed to land a commercial
aircraft manually (and safely) on the Hudson River in New York City
in January 2009.
The most sophisticated, quantitative forecasting tools are not
necessarily the most reliable. Whether a model is deterministic or
stochastic, it may not be very realistic, and its efficiency may be
highly dependent on market conditions. The well known proverb "no
risk, no reward" is certainly true, but potential profit should
outweigh potential loss in a risk-sensitive environment.
Market Risk Measurement Biases
Market risk measurement is based on value-at-risk (VaR), a model
which has recently been the subject of much criticism and which is
defined as the potential loss at a specific confidence level, over
a certain period of time. In most cases, VaR considers probability
of losses at either 95% or 99% confidence levels. For the latter,
this means that we expect that the VaR will be outstripped five
times over a two-year period. Financial institutions design their
own VaR model to take into account their constraints. It could be
historical, simulated or statistical.
Nassim Nicholas Taleb, a financial derivatives specialist and
former trader, has vigorously criticized VaR, describing it as "the
great intellectual fraud," partly because of its inability to
measure accurately "fat tail" events (also known as Black
It is difficult for any measurement tool to assign a probability
to these rare events, but the credit crisis has revealed one other
VaR flaw: liquidity risk measurement. VaR assumes normal market
conditions, where assets are liquid enough and the history is
meaningful. So it has recently proven very inefficient in the
measurement of illiquid instruments, like credit derivatives.
What About International Regulations?
In January 2009, fueled in part by numerous bankruptcy filings
in the financial services industry (which proved that some
financial institutions lacked the capital needed to cover
themselves in very illiquid markets), the Bank Committee on Banking
Supervision (BCBS) published consultative papers to amend the Basel
II capital accord. The amendments call for an increase in capital
charges and a decrease in the amount of leverage financial
institutions can hold.
Basel II, of course, was initially designed to improve risk
management and to enable banks to better align their risks with
their regulatory capital. The amendments show that the BCBS
recognize that capital has become a scarce and precious resource,
and also demonstrate that securitization will no longer be
recognized as a risk mitigation tool.
Back-testing and stress testing are among the other important
requirements of the enhanced Basel II accord. The validity of the
VaR assumptions can be assessed through back-testing, which
measures the exceedance of losses compared to the VaR number.
The stress testing requirement, meanwhile, underlines one of the
shortcomings of VaR -- its inability to measure exceptional market
moves beyond normal conditions. Stress tests play a significant
role in the quantification of downside risk versus business
opportunities; they make use of historical and hypothetical
scenarios that not only assess risk but also facilitate risk
Overall, in response to the crisis, risk managers have improved
risk analysis processes and general policies, and regulators have
taken some steps to enhance risk management practices.
While quantitative risk models definitely enhance the
decision-making process (as long as a model's assumptions are
clearly defined), their inherent uncertainty should encourage
financial institutions to use them more cautiously, to supplement
their use with qualitative analysis and to be more sensitive to
Ludovic Lelégard (FRM) is a risk manager at HSBC France,
Global Banking and Markets. He can be reached at
email@example.com. This article expresses the personal
opinions of the author, which are not necessarily shared by his
employer or any other entity.