How can the stock market be calm while, at the same time, individual stocks are going through one of the most volatile periods in history? It is not a paradox: it is a lesson in diversification that the market itself is teaching us in real time.
The gap between the fear index for individual stocks and the index itself is at record highs
The VIX, the well-known “fear index,” measures the volatility that the market expects for the S&P 500 over the next 30 days. At the beginning of August 2026, it is trading at around 16, a historically low level.
Apparently, total calm.
However, since the end of 2024, another, lesser-known gauge has existed: the VIXEQ, the Cboe S&P 500 Constituent Volatility Index. Instead of measuring the expected volatility of the index as a whole, it measures the average expected volatility of each of its constituent stocks, weighted by market capitalisation. Put simply, it is a “VIX for individual stocks.”
And the VIXEQ tells a very different story: it is trading above 44, close to its historical highs. The gap between the two indicators has never been so wide. The spread reached a record 34 points on 9 July and ended the month at around 28, approximately twice its average over recent years.
To ensure that the comparison does not depend on the general level of volatility, the figure in relative terms is even more revealing: the average S&P 500 stock is pricing in expected volatility 2.8 times higher than that of the index, after reaching a peak of 3.26 times in mid-July.
How can this divergence be explained?
The key lies in correlation. When stocks move independently, some rise while others fall. Their movements offset one another within the index, which barely moves even though each constituent may be fluctuating sharply.
That is exactly what is happening now. The implied correlation between S&P 500 stocks — which can be approximated from the relationship between the two indices — ended July at around 13%, after reaching a low of 9% on 10 July.
In plain terms, the options market is pricing in a scenario in which stocks move almost entirely independently of one another, something virtually unprecedented. For comparison, in 2023 this implied correlation averaged around 27%.
Why? Strategists point to two forces.
The first is artificial intelligence. Every launch of a new model reshapes expectations about which companies will capture value within the ecosystem: semiconductors today, software tomorrow and energy infrastructure the day after. Winners and losers rotate at an unprecedented pace, and market narratives amplify these swings.
July 2026 illustrated this perfectly with memory-chip manufacturers, which until only a few weeks earlier had been the hottest trade of the year.
SK Hynix, the global leader in HBM memory for AI servers, went from record highs at the end of June to losing more than 50% in five weeks, recording its worst month since October 2008 and the worst trading session in its history — down 15% on 28 July — only to rebound 30% in a single day on 31 July.
Micron fell 39% from its 25 June high, dropping from 1,213 dollars to 739 dollars per share and wiping out around 350 billion dollars in market capitalisation, before rebounding 18% in a single session.
And there is an important nuance: these declines came after vertical rises. Micron had more than doubled over the previous seven weeks and was still up more than 200% for the year. Moreover, the fundamentals had not broken down: the sector reported record results. It was the narrative that collapsed, not demand.
The episode also claimed a high-profile victim that encapsulates the entire story: Situational Awareness, the AI-focused hedge fund founded by Leopold Aschenbrenner, a former OpenAI researcher.
On paper, it was the best-positioned investor in the world to identify the winners of the AI boom: first-hand industry knowledge, around 20 billion dollars under management and a cumulative return of approximately 270% in 2026.
Yet July’s collapse hit its concentrated portfolio so hard that, on 30 July, it was forced to liquidate almost all of its listed positions, selling them to Citadel.
In fact, part of the rebound during the final session of the month was attributed precisely to the market learning that the forced seller was already out.
The lesson is uncomfortable but valuable: if even the most informed and successful specialist of the moment could not sustain a concentrated bet through the volatility that the VIXEQ had been signalling for months, what chance does the average retail investor have?
And what happened to the index in the meantime? The S&P 500 ended July almost flat. At its worst point during the month, it was down barely 4% from its June highs.
That is exactly the contrast being priced by the VIX–VIXEQ spread: an earthquake in individual stocks, calm in the overall index.
The second force is higher interest rates, which have raised the bar for every individual company. With capital becoming more expensive, the gap between strong and fragile businesses widens, and the market punishes or rewards them more severely than during the era of free money.
The lesson for long-term investors
This is where this technical phenomenon becomes highly practical.
Think about it for a moment: the options market itself — where some of the world’s most sophisticated investors operate — is putting a price on this reality. Picking which individual stock will perform well is now more uncertain than ever, while the diversified portfolio as a whole remains relatively stable.
An investor concentrated in a small number of stocks is exposed to average implied volatility of 44%. An investor in the full index is exposed to 16%.
The difference between those two figures — 44% minus 16%, or 28% — is, quite literally, the value of diversification trading in real time.
And this is not a temporary spike. The gap has been widening steadily for years.
The average annual spread between the VIXEQ and the VIX has risen from 14 points in 2023 to 18 in 2024, 19 in 2025 and 24 points so far in 2026.
The same trend can be seen in relative terms, with the average ratio increasing from 1.9 to 2.3 times over the same period.
In other words, the discount the market applies to concentration risk continues to grow, whichever way one looks at it.
Historically, the average S&P 500 stock trades with around 2.1 times the volatility of the index.
This is not a new idea. Academic research has documented for decades that a very small minority of stocks account for most long-term market wealth creation, and that identifying them in advance is extraordinarily difficult.
What is new is the scale: never before has the market quantified the risk of trying to pick winners so explicitly.
It is also worth remembering that this is not limited to small or speculative companies. It has happened, and will continue to happen, to the largest companies in the world.
Let us look at another dimension of risk: drawdowns from previous highs, as shown in the table above.
Amazon fell more than 90% after the dot-com bubble burst. Microsoft took more than a decade to recover its 2000 highs. Meta lost 76% between 2021 and 2022, and Nvidia fell 66% during that same year, shortly before becoming the leading company of the AI cycle.
Investors who held these stocks in concentrated portfolios experienced those collapses in full. Those who held them within a global index barely noticed them as a small, diluted fraction among hundreds of companies.
It is the same story as July 2026, repeated over decades: investing directly in an individual stock and investing through a diversified index are radically different experiences, even when the company ultimately becomes a success.
Or, as the famous maxim attributed to Harry Markowitz — Nobel Prize winner and father of modern portfolio theory — puts it, diversification is the only free lunch in investing, an idea he formalised in his foundational 1952 paper, “Portfolio Selection.”
Important note: the VIXEQ exists only for the S&P 500, because calculating the implied volatility of each constituent requires a deep options market for every individual stock, something that today only the US market provides. Other indices have measures of index volatility (the VSTOXX for the Euro Stoxx 50, the VXN for the Nasdaq-100), but not of their individual constituents. In any case, with the US representing around 70% of the MSCI World, the conclusion can reasonably be extrapolated to a global portfolio.
The relative calm of the index is no guarantee either
This should not be interpreted as meaning that “the index never falls”.
Unfortunately, although index volatility has historically been much lower, its “relative calm” compared with individual stocks is not always guaranteed.
During episodes of macroeconomic shock — think of March 2020 or the tariff turmoil of April 2025 — all stocks fall at the same time, correlations surge and the relative protection offered by the index declines dramatically.
The figures from April 2025 illustrate this precisely: at the height of the panic, implied correlation jumped from its usual level of around 25% to 56%, and the ratio between individual-stock volatility and index volatility compressed from almost 3 times to just 1.33 times, its lowest level in the past three years. The spread in points barely moved, but diversification cushioned losses far less than usual: when panic arrives, everything falls together for a while.
That is why diversification does not end with equities: a well-constructed portfolio combines global equities with fixed income (and ideally other uncorrelated assets) and is adapted to each investor’s risk profile and time horizon. Index volatility is lower than that of its constituents, but it is not zero, and the best defence against shocks remains the same as always: a diversified portfolio, low costs and a long-term horizon that allows investors to weather the storms without selling at the worst possible moment.
In any case, what the data analysed here tell us is that the risk of concentrating on a small number of stocks has never been so expensive; or diversification has never been so cheap in relative terms.
Important note:
This article was prepared by the inbestMe team with the support of AI tools. The data, text and conclusions have been verified by the inbestMe team.
