Event Contracts and Market Topology
Working Paper · March 2026
When does a prediction market become cheaper than the derivative it shadows?
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?
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.
Chapter II
The framework measures when emergent order displaces constructed order.
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.
What binds the transition?
Data provenance and quality
Event Contracts
across
Categories
2,889,424 rows of trade data from January 2024 to February 2026
Contributions and Key Claims
Chapter III
Results across categories, with detailed case studies in BTC and FOMC.
Bitcoin Event Contracts
Winner: Prediction Markets
VRP mean 4.83% (median 4.10%) · 20 contracts · 6 horizons · $219M volume
The highest-VRP liquid category. Prediction markets already cost less for most BTC binary hedges.
The January 2026 Natural Experiment
Five BTC contracts. Identical 4.08% VRP. Different strikes. Different volumes.
| Strike | Volume | Outcome |
|---|---|---|
| $100K | $13.3M | PM wins |
| $105K | $7.1M | PM wins |
| $110K | $4.9M | PM wins |
| $125K | $2.2M | PM loses |
| $150K | $32.8M | PM wins |
Same VRP, different depths. Four cross. One doesn't. The only variable is liquidity.
Federal Reserve Event Contracts
Threshold-heavy: Markets Still Converging
Sample statistics
VRP mean 0.52% · 12 contracts · 6 meetings · $2.2B total PM volume
Low VRP. Deep derivatives markets. Yet 3 contracts already cross, and 9 more sit at threshold.
FOMC: Convergence Timeline
March 2024
12pp
Options-PM spread
February 2026
<2pp
Options-PM spread
Liquidity compression outpaced volatility expansion.
But first: replicating the benchmark
Fed Funds options are the natural FOMC derivative. But they can't be analyzed.
The observable market is unobservable.
The replication pipeline
Without direct Fed Funds options data, the paper builds a 7-step pipeline:
Each step adds assumption risk. That's the cost of Tier 2 provenance.
Election Event Contracts
Winner: Prediction Markets
Sample statistics
17 events · 12 countries · SVEP median 0.480 · 1 structural loss · 4 liquidity-constrained
Remaining Categories Summary
PM Wins
vs
Mixed / Losses
12 at threshold · 1 marginal · 87 total event-contracts
Most losses are liquidity verdicts, not wedge verdicts.
The aggregate masks the gradient. High-VRP categories cross. Low-VRP categories converge. Binding constraints block the rest.
Full Category Scorecard
| Category | N | Wins | Thresh. | Loses | Median VRP | Binding Constraint |
|---|---|---|---|---|---|---|
| BTC | 20 | 12 | 2 | 6 | 4.10% | PM liquidity (thin) |
| Elections | 17 | 12 | 0 | 5 | SVEP† | PM liquidity + structural |
| Equity | 13 | 1 | 1 | 10+1† | 1.90% | PM liquidity (OTM) |
| FOMC | 12 | 3 | 9 | 0 | 0.50% | Narrow wedge, high vol. |
| Gold | 3 | 1 | 0 | 2 | 14.33% | PM liquidity |
| Silver | 3 | 0 | 0 | 3 | −10.19% | Structural (neg. VRP) |
| Other 5 | 19 | 1 | 0 | 18 | var. | PM liquidity (all Tier 3) |
| Total | 87 | 30 | 12 | 44 | + 1 marginal |
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.
Chapter IV
Not a collection of notebooks. A production-grade evidence system.
23,462
lines of source
14,971
lines of tests
756
test functions
58 Python modules. 42 test files. 5 live API connectors. SHA256-checksummed outputs.
Ingest → Transform → Report
Three-step DAG. Each step independently testable. Runs live or from 333 MB deterministic cache.
756 tests across four layers: unit, integration, contract, and statistical. CI on every push.
One command. Every result.
$ git clone && uv sync
$ ./RUN_FULL_BASELINE_AND_ELECTIONS.sh
$ sha256sum --check SHA256SUMS.txt
✓ all outputs verified
Add your own events. Plug in your own data. The pipeline runs the same way.
The goal is an open-source research pipeline for institutional risk transfer on prediction markets — so others can measure what we've started measuring.
The claim is falsifiable.
Every number in the paper traces to a deterministic output. SHA256 either matches or it doesn't. You don't evaluate the argument by trusting the author.
You run the pipeline.
When volatility risk premium exceeds execution friction, prediction markets displace derivatives as the preferred topology for institutional binary risk transfer.
The answer is structural.
Not behavioral. Not informational.
This has begun. It will accelerate.
Markets take the shape of their costs.
Working Paper V2
Complete. In review.
Full open-source release: tooling, datasets, pipelines, and the complete paper.
Research-grade tooling for anyone working on event contract economics.