Event Contracts and Market Topology
A theoretical framework and measurement apparatus for the future of institutional risk transfer on prediction markets.
Code, pipelines, and tooling release publicly after review.
Institutional risk is often binary.
Binary answers. Yes or no.
The financial system makes institutions hedge these risks with continuous instruments. A pension fund worried about a Fed surprise buys Fed Funds options and pays for the whole volatility path when it only cares about the endpoint.
Event contracts price the endpoint directly.
$1 if outcome occurs
$0 otherwise
No path. No Greeks. No volatility surface to rent.
The price emerges from aggregation. Dispersed participants post bids and offers. The market matches them. Done.
If event contracts are structurally cheaper, why haven't institutions adopted them?
Liquidity. In 2024, a $3M FOMC position cost ~13% to execute in prediction markets.
The cheaper instrument couldn't be accessed cheaply.
Prediction markets have been studied at two levels.
Neither asks: compared to what?
Contributions and Key Claims
Everyone cites Hayek (1945).
Prices aggregate dispersed knowledge. The telecommunications system of the economy. This grounds L2.
This paper goes to Hayek (1973).
Not just what markets know, but how they're structured.
Cosmos versus Taxis. Emergent versus constructed order. That's L3.
Taxis
Constructed Order
The volatility surface. Dealers build and maintain it using models, inventory, capital.
Someone runs the apparatus.
Cosmos
Emergent Order
The order book. Dispersed participants post bids and offers. The price is the result of participation.
No one runs it.
Both can price binary risk.
One charges for the infrastructure. One doesn't.
Cosmos fails loudly
Thin books, wide spreads. Visible on every screen.
Taxis fails quietly
By not printing anything you can analyze.
The structural cost of constructed order is measurable.
W = VRP + B + F
This is the Vega Wedge.
The tax embedded in derivatives that prediction markets avoid.
Prediction markets have their own tax: execution.
Spread, depth, market impact. In 2024, these were severe. A $3M position moved the market against itself.
By late 2025:
78%
FOMC cost reduction
57%
BTC cost reduction
The friction is compressing. Fast.
The Core Framework
The threshold tips when:
Wstructural > Cexecution
When the structural wedge exceeds execution cost, prediction markets win on total cost.
Structural cost is a tax.
You pay it every time, embedded in the instrument.
Execution cost is switching friction.
It compresses as liquidity arrives.
Capital migrates when the tax exceeds the friction.
Scope
This paper measures whether the economics favor prediction markets. Platform risks, legal integration, compliance workflows, capital efficiency: these are real barriers. We don't measure them.
Economic favorability is a necessary condition for adoption. Not a sufficient one.
Theory and measurement apparatus. Not mechanism design.
Not all categories cross at once.
VRP varies. Telegraphed FOMC decisions compress it. Contested elections spike it. Crypto volatility sustains it.
VRP is the segmentation variable.
High-VRP categories cross first.
The data confirms it.
Low VRP
FOMC
Mean VRP: 0.69% · Structural wedge: ~0.8%
Telegraphed decisions. Liquid derivatives.
0/6
The gap narrowed:
The trajectory is clear. The threshold has not crossed.
High VRP
BTC
Mean VRP: 4.3% · Structural wedge: ~4.2%
Elevated volatility. No central signal.
8/10
2 marginal · 0 derivatives wins
Gaps of +3 to +9pp in high-VRP periods.
The threshold has crossed.
High-VRP crosses. Low-VRP does not. As predicted.
BTC Event Contracts vs Derivatives
Zero derivatives wins. Two marginal. The threshold has crossed.
We tried to measure derivatives costs directly.
The native instrument for FOMC binary hedging is Fed Funds options. The market returned no data.
Not insufficient data. No data.
We checked Databento, CME, CFTC. Zero rows. Product omitted. Excluded.
The contract exists. The market does not.
Pricing occurs inside dealer relationships that emit no public traces. We built a 7-step proxy methodology to measure what could not be observed directly.
The measurement difficulty is itself a finding.
If prediction markets produced degraded signals, the cost comparison would measure a quality discount.
BTC touch markets test this directly.
Contracts at $75K, $90K, $100K, $110K, $125K create a synthetic options chain.
We extracted implied volatility from 2.4 million trades.
Fat tails. Skew. Regime dynamics.
November 2025 flipped from call-dominant to −54pp put skew in one period.
No dealers. No SABR. No apparatus.
The smile emerged from aggregation alone.
Prediction markets access the same distributional content that derivatives encode.
The smile exists in the underlying distribution. The apparatus merely reveals it.
The cost differential is not a quality discount.
It is apparatus rent.
Same distributional content. Different transmission cost.
Cost comparison alone does not change how policy-makers, institutions and the world thinks about prediction markets.
A theoretical framework that identifies prediction markets as a different form of financial infrastructure, with different properties, different failure modes, different observability, can.
The framework produces actionable readings: which categories are contestable, which are not yet, which direction the threshold is moving.
Implications
For policy: Prediction markets are not speculation with useful byproducts. They are a different form of financial infrastructure, with different properties, different failure modes, different observability.
For institutions: The structural wedge is measurable. The threshold is identifiable. High-VRP categories are already contestable.
For theory: Hayek's Cosmos/Taxis distinction applies to market microstructure. Emergent order can serve functions that constructed order currently monopolizes.
For practice: The tools exist. The liquidity is arriving. The question is when, not whether.
Working paper
V1 complete. In review.
Sample expansion: 16 → 60–80 events across categories. Gradient regression with statistical power.
Full open-source release: tooling, datasets, pipelines, and the complete paper.
The goal is an empirical research apparatus, so others can measure what we've started measuring.
Feedback sharpens the framework.