(Please note: The Kotok Report is now also available on Substack at https://dkotok.substack.com.)
We’re going to offer readers a series of specific discussion points about stock and bond markets, the US dollar, and interest rates. Please bear with us, as some of this work may be technical in nature.
For the first installment in the series, I thank Bill Kennedy, CIO of Riskbridge Advisors (https://www.riskbridgeadvisors.com), for permission to share with our readers Riskbridge’s research on volatility shocks. (Disclosure: I am a member of the Riskbridge Advisory Board.)
Bill Kennedy’s research group found developments in volatility that point to regime change. They inventoried and statistically measured the stock market shocks captured by the VIX. A detailed graphic of this examination can be found at the bottom of this commentary.
Thirty-nine volatility shocks occurred during the 18 years preceding the Great Financial Crisis (GFC) of 2008.
1990–2007
39 Volatility Shocks
Avg. Shocks/Year = 2
Avg. VIX = 18.7
Ninety-three volatility shocks have occurred with the GFC and since.
2008–2025
93 Volatility Shocks
Avg. Shocks/Year = 5
Avg. VIX = 20.0
Note the trend. Volatility is one of the inputs in the calculus of risk. The greater the degree of vol, the more the risk. The greater the frequency of the vol shock, the more the risk. One reason is that there is no infinite time horizon. If you had unlimited time, you could wait for mean reversion. But all investors, traders, speculators, including institutional money pools, have some time horizon. It can be a very short amount of time (hours or days), or it can be a longer time (multi-generational years in family office trusts or endowment funds for major institutions.) Example: Harvard University’s endowment fund was once considered a fortress that would never have to be invaded. That assumption has now been proven false.
Regardless of the type of investor, time is limited and not infinite. And vol shocks occur, often without much warning. So, the frequency of the shocks and the intensity of them are two factors that drive the risk-taking methodologies of any serious investor.
Investors tend to think in terms of “mean reversion” and believe that distributions of these various shocks are “normal.” We’re taught that way. Normal is a mathematical term we are accustomed to thinking about in terms of the conversion of a scatterplot of data points into probabilities. Nearly every investor has some idea of the mean (average) and of one or two standard deviations. Many now use “z-scores” as a modernized version of this concept. But, but, but — what if there is a regime change and the old normal has been replaced with a new normal?
The regime is different since the GFC. Why is that a debate? Here’s my explanation.
We invest in what is, and we try to figure out the why. In the graphic below, there are two versions of curves. One of them, on the right, shows three types of traditional bell-curve-shaped distributions. The mean is the same in all of them, but the rest of the data points are arrayed differently because of changes in the condition of the data. Remember, a curve is a plotting of the data points.
The other depiction shows the skewness. Skewness can be negative or positive. In this case the mean has shifted. Given Trump tariffs; a weakened dollar; and unknowns about deficits, debt, budgets, and wars, what makes anyone think that the old-style bell curve still reflects a depiction of what exists today? The work of Bill Kennedy shows that vol has increased substantially. And that change suggests that the bell curve has experienced kurtosis —that is, a sharper peak and heavier tails reflecting more outlying data points. Furthermore, the increased number of vol spikes suggests there is skewness. Skewness means that the tail risks are larger.
Put all this together, and the investor/observer sees wilder swings in market prices. Further, we don’t know how wild the ride will become because the mean has shifted and so has the curve. We are accustomed to mean reversion in the old paradigm. That worked then. Today, I think that old assumptions may be a trap.
(“Descriptive Statistics” | TryExponent.com,
https://www.tryexponent.com/courses/statistics-experimentation-questions/statistics-descriptive)
Here’s Bill Kennedy’s VIX vol shock data series.
(Riskbridge Advisors)
We hope readers found this missive thought-provoking. Please share with others who might be interested; and if you are new to the Kotok Report, please subscribe. More issues are coming in this series.
