It’s March 2021 and WTI is trading in the mid-$60s. Goldman’s Jeff Currie is calling for the start of a generational commodity supercycle on CNBC, and publishing it across GIR pieces. After picking up lunch, returning to the desk, and looking over missed Bloomberg chats, I watched thousands of $99/$100 dollar-wide WTI call spreads clearing the broker market for $0.02.

Without any context, this might seem like a very strange trade until you realize that it’s actually a hedge to the real underlying trade. When you buy a very tight call spread and pay close to nothing for it, you likely aren’t making a linear bet on oil. The payoff is binary: the structure either pays ~$1 at expiry or it pays nothing. What you’re holding is, conveniently enough, called a binary option. This tight call spread is just an exchange-listed wrapper for when binary contracts aren’t directly available as liquid standardized exchange-listed contracts. The actual buyer in the market almost certainly had a customized OTC binary on crude sitting on their books facing one of their own clients, and was trading this call spread as a hedge. The binary was the real underlying trade. That was 2021. Today, those same OTC instruments, stripped of institutional complexity and given a friendlier UI, are called prediction markets. Billions of dollars in monthly volume across leading prediction market exchanges. Retail traders are betting on whether WTI crosses $100, whether the Fed cuts rates in June, whether the Strait of Hormuz closes this year, or who wins March Madness.

Without having to change the financial instrument, and with the right branding (as prediction markets), binaries have become all the rage across retail and a growing number of institutions.

Teaching an Old Dog New Tricks

Binary options aren’t new. They’ve traded institutionally for decades, often over-the-counter, often on macro assets, and mostly in sizes large enough to justify the legal and operational headaches. What is new is scale and accessibility. And with those have come the creeping realization that the problems which kept binaries in the OTC exotic desk have not disappeared just because someone built an app around them.

A binary option is the most intuitive financial contract in existence. You pick an event, you pick a threshold (or market), you win or you don’t. There is no margin call, and no complicated Greeks to track (except maybe 1 or 2). Retail traders understand this immediately in a way they never quite grasped traditional dated options or commodity futures term structure. And in the right categories, prediction markets work as intended: in crypto and elections, they can result in price discovery and drive the market toward fair value quickly. But there are some critical things that remain missing. Until these are structurally solved, the billions in volume across @Kalshi and @Polymarket is mostly retail money betting on sports, with sidequest opportunities for armchair economists thinking of themselves as geopolitical and macro experts, and of course the occasional insider trade.

What’s the Holdup?

When an institutional trader wants binary exposure to a macro event through traditional finance, they could try going the OTC route, or they could try to replicate a binary: multiple options legs, strike management, thinking about balance sheet consumption, legal documentation, etc. Every layer adds some incremental cost that should be priced in. @lzminsky’s research paper “Seeing Like a Market” puts a name to this: the vega wedge, which is the inherent cost embedded in trading vanilla options that binary hedgers shouldn’t have to pay. When you buy a standard option, the price reflects the full distribution of how the underlying market might move between now and expiry. That volatility exposure is the whole point for many options traders. But a binary hedger doesn’t care about the path dependency at all. They have one question: did the thing happen or not? They’re paying for the journey when they only need the destination, and the vega wedge is the name for that overcharge. With prediction markets, you can sidestep this by buying a standard event contract directly. And in categories where this structural tax is large enough, prediction markets are often cheaper than the traditional alternative at institutional scale. Minsky’s empirical work across eleven categories, 87 event contracts, and five asset classes showcases exactly this. In Bitcoin binaries, this effective tax runs around 4.8%. In gold markets, it can range from 7-20%. Although the economics favor prediction markets in these high-friction categories, actual market share displacement has yet to be demonstrated.

So what is keeping institutional capital on the sidelines?

No B-S, No Silver Bullet

In traditional options markets, Black-Scholes goes beyond a simple pricing formula, and serves as something of a shared coordinate system: a common language for communicating risk, finding relative value across strikes and expiries, and structuring complex products.

Although the original model is itself imperfect (’all models are wrong, some models are useful’), it gives traders a common language to express market views and risk, and that turns out to be worth a lot. Prediction markets often lack that language, except in the few categories where volume is deep enough to generate one organically.

Minsky extracted a volatility curve from 2.4 million Bitcoin binary trades: fat tails, skew, regime dynamics, all emergent from the trading activity itself. Sufficiently liquid markets build their own pricing language from the bottom up. What that language actually measures depends entirely on the market type. In financial asset-linked contracts (will the S&P 500 trade above 6,900 by year-end), the implied probability tracks the underlying directly, and the volatility of that probability is essentially a synthetic vol surface. In political or macro event markets, what moves the probability is news headlines hitting the wire, and that information reaches participants heterogeneously: some see it faster, interpret it differently, or have it before it’s public. In sports, probability changes with discrete events (a goal, an injury, a foul...anyone watch Moneyball?) that diffuse to everyone simultaneously. This is a challenging problem (being explored by teams like @functionspaceHQ): probability volatility is information flow, and can look completely different across market types. Currently, the same options contract primitive is trying to capture fundamentally different ways of information regimes.

In most prediction markets, there isn’t sufficient liquidity to generate this volatility surface of probabilities. Pricing a binary on whether Iran closes the Strait of Hormuz means forming a view from headlines and intuition. And the liquidity provider problem runs deeper: LPs in these markets hold inventory through resolution and profit based on outcomes rather than earning spread and going home flat. This introduces directional risk, and naturally widening spreads.

Come on Ref, That’s Foul Play!

Even if you can price the binary, it still requires (1) an actual resolution and settlement framework, and (2) protections against inherent information asymmetry, to preserve reasonably fair markets. Kalshi, as a CFTC-regulated exchange, is building toward a resolution process rooted in legal jurisprudence: a committee iteratively refining outcome rules through edge cases, the same slow process that eventually made credit default swaps contractually workable after years of disputed settlements. Polymarket uses @UMAprotocol, where token holders debate outcomes and vote on resolution.

UMA works when the question is clean. It struggles when the market is settling on whether a geopolitical event technically satisfies a contractual definition while the underlying commodity is moving $4 a barrel on the same headline. Resolution risk is real and it’s priced into prediction markets, just inconsistently. Nobody has pre-built models for how resolution mechanisms break down on ambiguous, high-stakes events, which are the exact markets where getting it wrong is the most expensive.

The markets where prediction markets would add the most value: discrete geopolitical events, regulatory decisions, corporate actions, are exactly the markets where information is the most concentrated. Some specific cases come to mind: Four newly created wallets, with no prior transactions, made hundreds of thousands in March 2026 by placing high-conviction wagers on US-Iran ceasefire hours before the announcement.

An Israeli Air Force officer attended a classified briefing and shared the timing around strikes on Iran, with a civilian, days before they transpired. This was then used by the civilian to trade on Polymarket. An anonymous trader made $1.15m betting on Google’s 2025 Year in Search results (winning on 22 of 23 markets), after previously profiting by correctly predicting the exact release window of Gemini 3.0 Flash. Even if the evidence is circumstantial, the pattern is difficult to explain away.

Both Polymarket and Kalshi updated their rules in March 2026, prohibiting trading on stolen information or illegal tips. The trickier challenge is that in elections and sports, crowd knowledge can aggregate toward something useful. In macro or idiosyncratic markets, the people with actual information are exactly the ones you’d least want trading on it.

(Spending) Mo Money, Getting Mo Problems

In a mature derivatives market, margin is productive. The collateral you post against a futures position can earn yield, offset risk against other parts of a trade structure, or be swept into short-term money-market funds when idle. The whole system is built around keeping capital working.

Prediction market collateral sits still. The USDC posted on Polymarket, which is then used to enter into a prediction market position, doesn’t cross-margin against a perpetual futures position anywhere. Kalshi contracts don’t net against Treasury exposure. Positions can’t be borrowed against or used as collateral anywhere else. Every dollar in a binary waits in isolation until resolution.

This is actually why the HF desks in 2021 would express binary views through listed call spreads rather than going direct to OTC binaries. The call spread margined cleanly against the existing futures book. The OTC binary consumed dedicated capital with no offset. You get similar economics, but with very different capital and risk profiles. Until there is broader adoption of cross-margin and portfolio margin solutions with prediction markets, true institutional adoption remains constrained.

This is exactly the kind of problem that builders like @wendotmarket are focused on solving.

Sequencing Some Partial Solutions

So what are the most relevant problems in the prediction markets space: Fundamental trading frictions due to the ‘vega wedge’ No unified pricing framework across different market structures and types Idiosyncratic oracle resolution in complex markets Lack of protections against information asymmetry used for insider trading Lack of capital efficiency and margin benefits These are hard problems. But, they are also somewhat similar to problems that have been solved in other derivative markets, given enough time and institutional interest.

Cross-margin is the most directly implementable capability (although non-trivial in practice). Liquid prediction market positions offsetting related financial exposure, oil binaries netting against crude futures, rate event contracts against bond positions, within a single margin framework, would quite easily change the cost of capital calculation for institutional participants.

Learning to trade the uncertainty itself rather than just the outcome becomes a novel primitive. The ‘signal’ in a prediction market goes beyond where a contract settles, and looks at how violently the implied probability moved on the way there. A market that can express that: how much information arrived, how fast, and from which direction, starts to look like an actual risk management tool rather than a directional bet. The data to reconstruct the flow of probabilities exists in the trading activity, but the core product is still missing.

From this, you also begin to get correlation structure. Event markets don’t exist in isolation. Oil binaries move with Middle East geopolitical markets. FOMC outcomes correlate with inflation data surprises. Building products that capture joint probabilities across related events is how you get from a collection of disconnected point bets to something that functions like a way to express precise, yet multi-faceted views. That architecture doesn’t exist, yet.

KYC as an initial measure against insider trading is a natural solution. It’s a first order protection that won’t solve all instances of exploiting information asymmetry, but will certainly make it far more difficult to do so. If we want to go beyond this, the industry requires frameworks like ‘designated insider regime’, that only become possible from regulatory mandates or voluntary investments from existing platforms.

Most serious derivatives markets started as an interesting idea, usually bound together with questionable infrastructure. Prediction markets are still in that phase. The maturation of prediction markets, however, requires rebuilding the institutional plumbing that every serious derivatives market eventually needs.