NAILBA Perspectives Virtual Symposium Special Edition | Page 25

Dangerous curve ahead
In his New York Times best seller, The Black Swan: The Impact
of the Highly Improbable, Nicholas Nassim Taleb highlighted
a decades-old idea that financial markets do not follow a bell
curve. In fact, he suggested that using a bell curve as the basis for
navigating risk can be dangerous. This forced many major financial
institutions to adopt heavy-tailed risk models that more accurately
capture the frequency and impact of much more severe losses in
financial markets.
Heavy-tailed distributions approach zero at a slower rate and
can have outliers with very high values. They better represent the
actual risk in markets. The heavy-tailed model underestimated
risk in only 2.4% of cases.* However, in most consumer-level
conversations about risk, heavy-tailed risk models have yet to
become commonplace. Despite the fact that if you compare the
standard risk estimates that use a normal distribution and value at
risk methodology to a heavy-tailed model using an estimated tail
loss methodology, and apply it to all listed U.S. securities, you’ll find
that the common model underestimated risk in over 77.3% of cases.
*Piccinini, R. (2017, March). Remodeling Portfolio Risk Modeling,
Financial Service Professionals Newsletter.
Heavy-tailed model
Using rolling six-month periods of SPY returns through May 7,
2020. The common risk estimate would suggest a risk of 14.64%,
while the heavy-tailed model discussed above would suggest a risk
of 51.79%. The worst six-month period during the financial crisis
was -45%, and there were 132 rolling six-month periods during that
time. The average of those periods exceeding the common risk
estimate was -33%, more than double that common methodology.
The 2020 pandemic is another great example. The worst six-month
period was -24% (as of May 7, 2020), and the average of the 17
periods in excess of the common estimate was -18%.
A good risk estimate that instills confidence and tempers
irrational fear should be based on a heavy-tailed model and an
estimated tail loss methodology. Using outdated or oversimplified
risk metrics leads to doubt and panic during market volatility. And of
course, the last thing you want to happen when the market dips, is
for your client to panic and sell at the worst time, locking in a loss. A
good advisor should help clients avoid those behavioral mistakes by
using quality risk metrics to effectively estimate downside risk and
prepare for those big market swings, while building client portfolios
that address both risk tolerance and risk capacity.
In order to effectively plan for
both risk tolerance and risk
capacity, an advisor must have a
good estimate of the downside
exposure that is actually present
in a client portfolio.
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