The Clerks’ Betrayal
Taleb criticizes treating financial risk as Gaussian when extreme market moves are much more frequent than that model suggests. He argues that a familiar numerical measure can become an anchor despite evidence against its assumptions.
THE IDEA
The Clerks’ Betrayal
I ended Chapter 1 with the stock market crash of 1987, which allowed me to aggressively pursue my Black Swan idea. Right after the crash, when I stated that those using sigmas (i.e., standard deviations) as a measure of the degree of risk and randomness were charlatans, everyone agreed with me. If the world of finance were Gaussian, an episode such as the crash (more than twenty standard deviations) would take place every several billion lifetimes of the universe (look at the height example in Chapter 15). According to the circumstances of 1987, people accepted that rare events take place and are the main source of uncertainty. They were just unwilling to give up on the Gaussian as a central measurement tool—“Hey, we have nothing else.” People want a number to anchor on. Yet the two methods are logically incompatible.
When to use it
Examining whether a portfolio risk model adequately represents extreme market moves.
Checking whether an insurance loss model captures catastrophic losses.
Testing engineering safety assumptions against plausible rare failures.
Assessing whether a model of extreme weather represents the relevant tails of the distribution.
FROM THEORY TO EVERYDAY LIFE
In practice
1987 Stock Market Crash
Taleb describes the crash as more than twenty standard deviations from the mean. Under the Gaussian assumptions he criticizes, such an event would occur only every several billion lifetimes of the universe.
Height Example
Taleb refers to a height comparison in Chapter 15, but the comparison itself is not included in this excerpt.
A MOMENT TO REFLECT
Put it to work.
How might your reliance on standard deviations mislead you in assessing real-world risks?
What rare events have you overlooked because they fell outside your Gaussian assumptions?
In what domains besides finance might the Gaussian model be dangerously inappropriate?
Often confused with
CONTINUE THE CONVERSATION
Source texts
- The Black Swan The Impact of the Highly Improbable With a new section On Robustness and Fragility (Nassim Nicholas Taleb)