Event Gamma - Trading the World as a Digital Option

AlgoQuantHub Weekly Deep Dive

Author

Nicholas Burgess
April 10, 2026

Welcome to the Deep Dive!

Each week on The Deep Dive we explore cutting-edge ideas in algorithmic trading, quantitative research, and modern financial engineering.

This week, against the backdrop of the current US–Iran conflict and its impact on live financial markets, we explore how geopolitical events are priced as implicit digital options—and how prediction markets can be used to extract those probabilities in real time.

Bonus content, we walk through trading oil during a key moment in current hostilities, around a binary geopolitical outcome: either a US-imposed deadline results in a negotiated peace deal with Iran, or tensions escalate into a more severe conflict.

Table of Contents

Feature Article: Event Gamma - Trading the World as a Digital Option

Most macro events are not smooth, continuous processes—they are discrete regime shifts. Consider the current US-Iran conflict will there be escalation vs de-escalation, recession vs soft landing, inflation shock vs stability. Yet markets often appear to move gradually into these outcomes. A more precise way to think about this is that markets are continuously repricing the probability of discrete states. In quantitative terms, this is exactly what a digital option does: it pays 1 if an event occurs, and 0 otherwise. Its price is therefore the market-implied probability of that event. The key insight is that many real-world trades—especially in macro—are implicitly exposed to these digital payoffs.

This is where prediction markets like Polymarket become interesting. Unlike options markets, where probabilities are embedded and must be extracted via models, prediction markets display them directly. A contract trading at 0.35 can be interpreted as a 35% probability of an event occurring. While these are not strictly risk-neutral probabilities (and can be influenced by sentiment, liquidity, or positioning), they provide a real-time signal of how the market is updating its beliefs. For a trader, this offers a new dimension: instead of inferring probabilities indirectly from price action, you can observe them explicitly and track how they evolve as information arrives.

This leads to a powerful reframing of trading: you are not trading price, you are trading state probabilities. Positions in assets like equities, oil, or credit can be understood as bundles of exposures to different macro “states.”

If we consider, the current US-Iran conflict, an oil position, for example, behaves like a direct play on a geopolitical digital—if supply disruption risk rises, oil prices can spike sharply; if tensions ease, prices can fall just as quickly. This introduces a concept I think is underappreciated: event gamma. Just as option gamma measures sensitivity to changes in the underlying price, event gamma measures sensitivity to changes in probabilities. When probabilities shift rapidly—say from 30% to 60%—assets with high event gamma can move sharply, even if the underlying event has not yet occurred. Understanding and monitoring this dynamic can provide a meaningful edge in navigating volatile, event-driven markets.

The natural question then is: if prediction markets reflect real-world probabilities, and options markets reflect risk-neutral probabilities, is there an arbitrage opportunity? Not in the pure, risk-free sense—but there is a form of statistical arbitrage. These two markets are pricing the same event through different lenses. Prediction markets capture belief and information flow; options markets embed fear, hedging demand, and capital constraints. At times, one market will react faster than the other. When those views diverge, the gap becomes informative. If options are implying a much higher chance of disruption than prediction markets, it often reflects expensive hedging—investors paying up for protection. In that case, it can make sense to sell convexity—for example, by selling upside in oil through call options or spreads—effectively betting that the feared extreme outcome is overpriced. If the opposite is true, and prediction markets move first while options lag, then buying convexity—owning options that benefit from large moves—can be attractive, as markets may be underestimating the potential for a sharp repricing.

More importantly, the real opportunity often lies not in the level of probabilities, but in how they change. If a prediction market suddenly moves from 30% to 50% probability of escalation, and oil hasn’t fully reacted yet, you are observing a shift in belief before it is fully expressed in price. In other cases, liquid markets like oil or options may move first, with prediction markets catching up more slowly. This is where the idea of event gamma becomes practical: assets don’t just respond to events, they respond to changing expectations of events. By tracking which market is leading and which is lagging using Granger causality tests say, you can position ahead of the adjustment—buying assets likely to benefit from rising risk, or reducing exposure as risk fades. In this sense, the edge is a form of statistical arbitrage on probabilities: you are exploiting temporary mismatches between how different markets price the same underlying event. You’re no longer waiting for the outcome—you’re exploiting information lags and trading the market’s evolving expectations in real time.

Keywords: Digital Options, Prediction Markets, Event Gamma, Regime Shifts, Macro Trading, Implied Probability, Statistical Arbitrage, Convexity

Bonus Article: Trading Oil as a Geopolitical Digital Option

Consider the US-Iran conflict and the US imposed deadline for peace deal or major escalation—and further disruption to key oil shipping routes—is a real possibility, but not a certainty. From a modelling perspective, this is a classic binary setup: either escalation occurs, restricting supply and pushing oil prices higher, or de-escalation occurs, restoring stability and easing prices. Suppose a prediction market assigns a 40% probability to escalation. A trader holding oil futures is implicitly long that 40% tail scenario—benefiting if supply shocks materialise—while being exposed to downside if the probability collapses.

Now imagine new information arrives and the probability of escalation drops from 40% to 20%. Even though the event has not yet resolved, the expected payoff of the disruption scenario has halved. Markets reprice immediately: oil prices fall as the likelihood of supply constraints diminishes. This is event gamma in action—the sensitivity of asset prices to changes in event probabilities rather than realised outcomes. By monitoring probability shifts (via prediction markets or inferred from options), traders can position not just for outcomes, but for changes in belief. That’s the edge: you’re not waiting for the tanker routes to reopen—you’re trading the probability that they will.

A practical way to express this is to focus on the build-up to the event rather than the outcome itself. If probability is evolving over time—for example drifting from 40% to 25% over a day or two—and oil has not yet fully repriced, a trader could enter a position that benefits from further adjustment, such as a short (or long) oil futures position or a more controlled structure like a put spread (or call spread). Crucially, the position is not held into the binary resolution. Instead, it is exited ahead of the deadline, once the market has had time to incorporate the updated probabilities. The objective is to monetise the repricing of belief, not the event itself.

Importantly, these probabilities do not need to be traded directly through oil; for broad geopolitical events, the same signal can be expressed across multiple asset classes—such as the S&P 500, rates, or FX—by selecting instruments with the highest sensitivity to shifts in the underlying risk regime.

To conclude, event gamma is best thought of as a form of statistical arbitrage on probability dynamics rather than a directional bet. In practice, it requires discipline: entering early enough that probabilities are still moving, and exiting before the distribution collapses into a binary outcome where gaps dominate and edge disappears. The framework is also testable—by analysing how assets respond to changes in event probabilities over time, one can build a repeatable strategy around when markets tend to lag and how quickly they catch up. The edge, if it exists, lies in that lag—not in predicting the final outcome.

Keywords: Oil Trading, Geopolitics, Event-Driven Trading, Probability Shifts, Energy Markets, Trading Framework

Algo Quant YouTube Channel

Click here for Algo Trading & Quant Research Channel YouTube playlists include:

  • Interest Rate Markets

  • Bond Markets

  • Credit Derivatives

  • Monte Carlo Simulation

  • Advanced Quant Models

  • American Option Trading

  • Live Algo Trading with IB Broker

Feedback & Requests

I’d love your feedback to help shape future content to best serve your needs. You can reach me at [email protected]