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This week, Jeff Skilling received a well-deserved
sentence of 24 years for his creativity at Enron. I wouldn't take the
sentence too seriously. Remember that Mike Milken—the Junk Bond King of
the 1980s—was sentenced to ten years. After two years, he was diagnosed
with terminal cancer, petitioned for early release, received early
release, and then had a miraculous recovery. Today, he is hobnobbing
with the rich and powerful through his personal foundation.
At least Skilling did us the favor of pushing
Amaranth from the popular headlines. But professionals will continue to
sift through that blow-up's aftermath for some time to come. And
predictably, the recurring debate about value-at-risk has, well,
recurred.
It started when Hilary Till of Premia Capital
Management released a damage control piece assessing aspects of the
Amaranth case. She assured us that the market move associated with
Amaranth's loss was a nine standard deviation event.
If you don't know what that means, it is statisticians' speak for "it
won't happen again."
In somewhat of a rebuttal piece, Chris Finger of
the RiskMetrics Group pointed out that Till's nine standard deviation
claim depends on how she chose to calculate one standard deviation. If
you calculate a standard deviation using historical data, you will get a
different result if you use three months of data or six months. You will
get a different standard deviation if you equally weight the data or
give greater weight to more recent data.
Finger presented the following chart of the
three-year history of a particular natural gas spread Amaranth was
exposed to. It also shows the standard deviation of that spread
calculated over time using an exponentially-weighted moving average (EWMA).
In the chart, the EWMA standard deviation rises over time, reflecting
the increasing volatility of the spread. Because it weights recent data
more heavily, the EWMA indicates a higher standard deviation in the days
leading up to Amaranth's distress than would a uniformly-weighted moving
average (UWMA). With a higher standard deviation calculated using the
EWMA, Amaranth's loss becomes a three standard deviation event rather
than the nine standard deviation event Till obtained using (presumably) a UWMA. Anyway, that is what
Finger concluded.

Now for some bad news. This is just one example.
Before we all go off and implement EWMA in our value-at-risk (VaR)
measures, we might want to contemplate other cases. EWMA tends to
perform well when volatility gradually increases from a modest
level—precisely the case Finger considered. However, EWMA can give a
false sense of security if there is a brief lull in the market. In a
scenario where there is a sudden market move in the midst of a market
lull, EWMA will perform poorly compared to UWMA. I discuss the relative merits
of UWMA and EWMA in my book Value-at-Risk: Theory and Practice.
Honestly, both UWMA and EWMA are crude. We use them only because there
are no good alternatives. What is needed is high-dimensional GARCH or
stochastic volatility models. The biggest problem with VaR today—and
this problem has festered unresolved for ten years now—is the lack of
such sophisticated high-dimensional time series models.
What does this mean for users of VaR measures? That
depends on what you use the VaR measures for. VaR is just a tool. Like
any tool, it is useful for certain things and not useful for others.
Screw drivers are also tools. They are useful for driving screws but not
useful for driving nails. The next time you hear someone dumping on VaR,
ask him why he isn't dumping on screw drives as well.
One group whose expectations for VaR far exceed its
abilities is bank regulators. They want VaR to represent the 99%
quantile of a trading book's ten day loss. Philosophically, that is like
asking for the third decimal place of the probability of rain five days
from now. There is no meaningful difference between a 12.4% probability
of rain and a 12.7% probability of rain. Likewise, there is no
meaningful difference between a ten-day 99% VaR of $4 million or $7 million. A
ten-day 99% loss is something that happens maybe twice a decade. It is
not something you can make precise probabilistic assertions about. I'm
sorry if I am bursting any bubbles here. I would be okay with one-day
99% VaR or ten-day 90% VaR, but ten-day 99% VaR isn't meaningful. We can
talk about it the same way we talk about unicorns, but that doesn't make
it real. Of course, bank regulators are in the habit of getting what
they ask for, so banks calculate large numbers, dress them up in all
sorts of mumbo jumbo about extreme value theory or copulas, and call
them ten-day 99% VaR. They even cobble together "backtests" that
purportedly "validate" these numbers.
That is bank regulation, but I am more interested
in financial risk management—there is a difference. For monitoring
market risk, one-day 95% VaR is fine. It is also meaningful—in the same
way that there is a meaningful difference between a 40% probability of
rain and a 70% probability of rain. Also, for risk management purposes,
the actual numbers produced by a VaR system aren't so important as is
their trend. If you calculate one-day 95% VaR consistently from one day
to the next, when the number jumps, you know something is going on. If
the number gradually increases, doubling over a two-week period, you
know something is going on.
Forget about distinguishing between three standard
deviation or nine standard deviation market moves. Forget about the
third decimal place of the probability of rain. Forget about ten-day 99%
VaR. Instead, watch how your VaR numbers change from one day to the
next. There is an old saying on Wall Street that is worth retreading for
financial risk management: "The trend is your friend." For monitoring
market risk, the absolute level of your VaR numbers isn't that
important. Watch how they trend.
Glyn A. Holton
See
Hilary Till's article.
See
Chris Finger's article.
See
Glyn Holton's book.
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