Nassim Taleb has long been a critic of traditional forecasting methods like the ones underlying these stress tests. He even coined a now oft-repeated term to capture his criticism – “black swan” – which became a huge New York Times bestselling book.
Now, he warns that “fragility is especially high for the banks with the worst outcomes” according to a new metric he’s developed to better analyze the risks facing the banks.
In a new white paper with researchers at the IMF, Taleb explains the reason why all of the stress tests conducted by central banks and international financial institutions like the Federal Reserve, the ECB, and the IMF come up short:
First, many stress tests focus on the point estimates of very few scenarios, and often pay little attention to how the impact would change in case of different scenarios, e.g., a slightly more severe one. Second, if stress tests do not take into account the possibility of model and parameter error, it can be misleading to rely only on the point estimates of even well-designed stress tests. Without considering the potential for these errors, one could miss the convexities/non-linearities that can lead to serious financial fragilities.
A better approach, according to Taleb and his IMF co-authors Elie Canetti, Tidiane Kinda, Elena Loukoianova, and Christian Schmeider, is to measure the difference between outcomes arising from different scenarios instead of focusing on the estimates of potential losses themselves.
According to Taleb, this is the real way to measure the “fragility” of a bank or a country in the event of a negative economic shock. Because point estimates are so prone to errors from faulty model assumptions, measuring the distance between them to detect how quickly losses pile up as the economic shock gets larger becomes a vastly more reliable measure of risk.
In other words, it’s not the size of the losses themselves that is important. Instead, it’s the rate of change of potential losses as the economic situation deteriorates that determines how fragile a bank is, by Taleb’s standards.
The one percent of the one percent of the population is vastly more sensitive to inequality than total GDP growth (which explains why the superrich are doing well now, and should do better under globalization, and why it is a segment that doesn’t correlate well with the economy). For the super-rich, one point of GINI causes an increase equivalent to 6-10% increase in total income (say, GDP). More generally, the partial expectation in the tail is vastly more sensitive to changes in scale of the distribution than in its centering.
Sellers of luxury goods and products for the superwealthy profit from dispersion more than increase in total wealth or income. I looked at their case as a long optionality, benefit-from-volatility type of industry.
Another business that does not care about the average but rather the dispersion around the average is the luxury goods industry—jewelry, watches, art, expensive apartments in fancy locations, expensive collec – tor wines, gourmet farm – raised probiotic dog food, etc. Such businesses only cares about the pool of funds available to the very rich. If the population in the Western world had an average income of fifty thousand dollars, with no inequality at all, the luxury goods sellers would not survive. But if the average stays the same, with a high degree of inequality, with some incomes higher than two million dollars, and potentially some incomes higher than ten million, then the business has plenty of customers—even if such high incomes were offset with masses of people with lower incomes. The “tails” of the distribution on the higher end of the income brackets, the extreme, are much more determined by changes in inequality than changes in the average. It gains from dispersion, hence is antifragile.
This explains the bubble in real estate prices in Central London, determined by inequality in Russia and the Arabian Gulf and totally independent of the real estate dynamics in Britain. Some apartments, those for the very rich, sell for twenty times the average per square foot of a building a few blocks away.
Harvard’ s former president Larry Summers got in trouble explaining a version of the point and lost his job in the aftermath of the uproar. He was trying to say that males and females have equal intelligence, but the male population has more variations and dispersion (hence volatility), with more highly unintelligent men, and more highly intelligent ones. For Summers, this explained why men were overrepresented in the sci – entific and intellectual community (and also why men were overrepre – sented in jails or failures). The number of successful scientists depends on the “tails,” the extremes, rather than the average. Just as an option does not care about the adverse outcomes, or an author does not care about the haters.
A reader has sent in a copy of Nassim’s Lecture notes from when he was teaching a course at the University of Massachusetts, Amherst, MA in 2005. The course/lecture series are titled: Randomness, Decisions, and Human Nature (SOM 797R – SYLLABUS).
Unfortunately all the links within the PDF are missing, if anyone has a copy with all the working links to studies, research papers, books, articles, images, etc, please let us know!
A spurious tail is the performance of a certain number of operators that is entirely caused by luck, what is called the “lucky fool” in Taleb (2001). Because of winner-take-all-effects (from globalization), spurious performance increases with time and explodes under fat tails in alarming proportions. An operator starting today, no matter his skill level, and ability to predict prices, will be outcompeted by the spurious tail. This paper shows the effect of powerlaw distributions on such spurious tail.
The Green Lumber Problem, outlined in Nassim Taleb’s upcoming book Antifragile, is essentially misunderstanding which facts are relevant vs those which are not in regards decision making under uncertainty.
“In one of the rare noncharlatanic books in finance, descriptively called What I Learned Losing A Million Dollars, the protagonist makes a big discovery. He remarks that a fellow called Joe Siegel, the most active trader in a commodity called “green lumber” actually thought that it was lumber painted green (rather than freshly cut lumber, called green because it had not been dried). And he made a living, even a fortune trading the stuff! Meanwhile the narrator was into theories of what caused the price of commodities to move and went bust.
The fact is that predicting the orderflow in lumber and the price dynamics narrative had little to do with these details —not the same ting. Floor traders are selected in the most nonnarrative manner, just by evolution in the sense that nice arguments don’t make much difference.”