At this year’s Swiss Quantitative Investors Summit, organised by BNP Paribas Global Markets, at Château de Salavaux in Neuchatel, our Fund Manager Simon Lépine joined Julien Turc, Head of Quantitative Strategy Lab at BNP Paribas, for a deep-dive conversation titled “One strategy, many faces: Cracking the Equity Dispersion Code.” Here’s a look at the key points from their discussion.
Rethinking a misunderstood strategy
Just a year ago, many in the market were ready to write off dispersion strategies. “Back in spring 2024, sentiment was very negative,” Julien recalled. “Implied correlations were falling, and the financial press was full of skeptical headlines. But despite that, our indices held up remarkably well.”
Julien explained that the confusion partly comes from how investors think about what drives dispersion. “Many people assume correlation is the key driver,” he said. “But historically, dispersion has been driven much more by idiosyncratic volatility. When return dispersion between U.S. stocks is low, our data shows it often has room to expand.”
When return dispersion between U.S. stocks is low, our data shows it often has room to expand, shared Julien Turc.
Simon agreed, adding a practitioner’s perspective: “Correlation levels alone don’t tell you much about PnL,” he said. “In practice, the composition of the basket matters far more. The S&P’s concentration in the so-called ‘Mag 7’ has actually pushed single-stock volatility higher while lowering overall correlations.”
In other words, low implied correlation does not automatically make dispersion riskier, and high correlation is not necessarily safer. What matters is how the strategy is built.
Is dispersion really crowded?
From theory to practice, the conversation moved on to how dispersion is actually traded in the market today. “More investors are active in dispersion now,” Simon noted. “The strong performance we saw in 2022 attracted a mix of pod shops and traditional institutional players. And banks, including BNP Paribas, have helped make it more accessible through QIS platforms.” But he added that this has changed how the strategy is implemented, not how much capital is involved.
“Total flows haven’t really grown,” he said. “They’ve just shifted toward simpler, more vanilla implementations, while more complex volatility swap structures have struggled.”
On single-name volatility, dispersion flows are still much smaller than overwriting programmes or reverse convertibles. On the index side, flows remain driven mainly by hedging demand, particularly at longer maturities.
“Importantly, the strong results we’ve seen recently have come from realised gains, not implied ones,” Simon added. “That tells me current pricing still looks attractive.”
Choosing the right implementation
They also explored a crucial point often overlooked: the way dispersion is implemented can influence performance even more than the market backdrop. “Historically, vega-flat has been the market standard,” Julien explained. “According to the QIS Lab, gamma-flat dispersion is conceptually simple to understand. It monetises idiosyncratic volatility and fits neatly into a portfolio, like buying volatility at an attractive price”
Simon described how the two approaches behave differently: “Gamma-flat is basically vega-flat plus a long volatility position on the index,” he said. “It automatically reduces its long vol exposure after volatility spikes, which acts like a natural take-profit mechanism.”
While vega-flat has near-zero market correlation and often outperforms over the long run, Simon sees value in combining the two: “I like to use gamma-flat on shorter maturities to capture realised dispersion, and vega-flat on longer maturities to monetise correlation premia,” he said. “Blending them lets you adapt to market conditions and specific portfolio needs.”
Dispersion is a toolkit, not a template
Both speakers agreed that dispersion should be seen less as a single strategy and more as a toolkit of possible implementations.
When you trade listed dispersion, you’re often managing thousands of options,” Simon pointed out.
“When you trade listed dispersion, you’re often managing thousands of options,” Simon pointed out. “There are countless ways to structure it – different weighting schemes, delta-hedging approaches, stock selection – and that makes design discipline crucial.”
Julien added: “If your single-stock volatility basket deviates from the benchmark, you’re effectively timing the strategy. That can work well as long as you control beta and size. At that point it starts to look more like a long/short equity volatility strategy.”
Both also noted the challenge of limited data. Listed options usually have maturities of six months, which creates only a short overlap of historical data per contract, and the reliable history spans just 15 to 20 years.
Precision over consensus
The discussion in Neuchâtel highlighted that dispersion is not “over” – it is simply misunderstood. It is neither defined by correlation nor doomed by crowding. Instead, it is shaped by how it is built and managed.
As Simon summed up: “Dispersion is one of those areas where every detail counts. Success depends less on chasing signals, and more on implementing the strategy with precision.”
