Flirting with Models

My guest in this episode is Lucas Schuermann, co-founder of Variational, which seeks to bring the trillion-dollar OTC derivatives market on chain.
This is my second episode on Variational. A little over a year ago I spoke with Lucas's co-founder, Edward Yu. In that time, the mission hasn't changed, but a lot else has.
We begin with a re-introduction to the business and its retail-facing platform, Omni. Unlike a central limit order book, where a crowd of competing market makers quote against unknown flow, every trade on Omni is quoted by a single, internal liquidity provider — OLP — through a request-for-quote model that segregates flow. Because OLP knows who it's trading with, it can price retail flow as non-toxic: netting offsetting positions against each other, warehousing what it chooses to keep, and hedging only the residual risk externally. It's a model that let Variational list a long tail of crypto-native assets at competitive spreads, even where on-platform open interest remained thin.
The surge in demand for real-world asset exposure on chain has pushed that model in two directions.
The first is widening where those hedges can go. Rather than trying to rebuild forty years of traditional market depth on a crypto order book, Variational has built a global network of dealer relationships to tap into it directly.
The second is questioning whether the perpetual future is even the right instrument. Perp funding is driven by where the contract trades relative to its index, which makes it volatile and hard to forecast. Variational's answer is a swap: a price-return leg plus an explicit financing leg, priced off short rates — a far more predictable cost of carry for anyone who wants to hold levered exposure for the long haul. And there's currently over a billion dollars of dealer capacity behind it.
I hope you enjoy my conversation with Lucas Schuermann.

My guest this episode is Stacie Mintz, Managing Director and Head of Quantitative Equity at PGIM Quantitative Solutions.
Stacie has spent 33 years at PGIM, and she's been there for every defining moment of the firm's quant equity effort: a first strategy born from a client's challenge to move beyond indexing, a 1999 decision to abandon Barra and bring the risk model in-house, surviving the quant quake of August 2007, and the post-GFC realization that in a crowded-factor world, it's not enough to be a quant — you have to be a different quant.
We dig into what PGIM's "fundamental quant" label actually means in practice, from a financing factor that asks how a company funds its growth, to a factor taxonomy that includes an unfamiliar Linkages group and — unusually for a quant shop — excludes momentum entirely.
In the back half, we turn to the frontier: turning qualitative signals like board composition and innovation into systematic factors, building models that assess emergent shocks like COVID and AI in real time, and why Stacie calls LLMs "bazookas" — tools powerful enough to blow up what already works, which is exactly why you start with the insight and only then reach for the tool.
Please enjoy my conversation with Stacie Mintz.

My guest this episode is Ben Wellington, Head of Complex Feature Engines at Two Sigma.

In a modern quant process, one might argue that edge can live in three places: the data you can get your hands on, what you do with that data, and how you forecast from it. Ben lives squarely in the middle layer — feature generation — which also happens to be the place AI is reshaping fastest.
So that's where we spend our time. We start with what a "feature" even is, and why, as raw data gets commoditized, the edge increasingly comes from what you build out of it. Then we follow the thread running through the whole conversation: large language models. Ben has a line I keep coming back to — that anything can be language now — and we trace what that unlocks, and whether making feature creation this cheap just democratizes the edge away.
We close on what a quant starting out today should be building toward, and which skills compound most in a career where AI is the dominant tool.
Please enjoy my conversation with Ben Wellington.

My guest this episode is Peter Hecht, co-head of the North America Portfolio Solutions Group at AQR.
Portable alpha is drawing serious attention right now. Long the province of large institutions, it's now being wrapped into mutual funds and ETFs — which means, for the first time, advisors and the individual investors they serve get a seat at the table. So I wanted to sit down with someone who's been thinking about the idea for almost two decades.
We start with the basics — what portable alpha actually is, and why Peter insists the core concept is funding, not leverage. From there we work through the real risks, the cautionary tale of 2008, and the design space: how you organize the alpha and the beta, which overlays to choose, and how to combine them without smuggling in hidden tail correlation.
Then we turn to what matters most for this new audience — sizing, lifecycle, rebalancing inside a daily-liquid wrapper, and the gap between the line-item experience and the actual portfolio impact.
Please enjoy my conversation with Peter Hecht.

My guest today is John Gu, founder and CEO of Caladan, one of the most active market makers in crypto and a firm that has provided liquidity to more than 200 token launches. John's path runs through MIT, AlphaSimplex, Citadel's principal strategies group, and Tower Research before landing in Singapore at the dawn of the ICO era — where what started as a trade on the kimchi premium became the foundation for one of the most active liquidity providers in digital assets.
The thesis of our conversation is what I'll call the cold start problem. In traditional markets, every newly listed stock arrives with scaffolding already in place — a designated market maker, a reference price, a universe of comparables, and decades of regulatory infrastructure. Crypto inverts that. A new token can launch with no orderbook, no comparables, and no clear demand curve. Someone has to quote a two-sided market into that void, and how they do it shapes whether the asset becomes a real, tradable thing — or a graveyard of wide spreads and stranded liquidity.
John and I dig into how you bootstrap liquidity from zero, how the quoting playbook evolves as a market matures, the economics of token market making contracts, and how that same infrastructure now bridges into structured products and treasury solutions for token foundations.
Please enjoy my conversation with John Gu.