Event Contracts and Market Topology
March 2026
When does a prediction market become cheaper than the derivative pricing the same risk?
Institutional risk is often binary.
Binary answers. Yes or no.
The only available instruments are continuous. 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 could exceed ten percent of notional in prediction markets.
The cheaper instrument couldn't be accessed cheaply.
TOPOLOGY EXPLORER
The complete evidence surface.
Every contract. Every cost comparison. VRP, spread, depth, and cost basis — exposed at institutional scale across 87 event contracts. The framework becomes navigable.
30 wins · 12 at threshold · 45 losses
87 CONTRACTS · 11 CATEGORIES · 5 ASSET CLASSES · 2.9M TRADES
Open the Explorer →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 friction: 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
Zero losses. Three already crossed.
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.
The plumbing has not arrived. The demand has.
FOMC: Convergence Timeline
March 2024
12pp
Cost gap at $3M
February 2026
<2pp
Cost gap at $3M
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:
The seven steps are not methodology. They are the phenomenon.
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
At Threshold
PM Loses
42 of 87 favorable at $3M reference depth.
Most losses are liquidity verdicts — the wedge exists, depth doesn't.
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 strikes from $75K to $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.
Every number traces to a deterministic output. You don't evaluate the argument by trusting the author — you run the pipeline.
When the structural cost of constructed order exceeds execution friction, prediction markets displace derivatives as the preferred topology for institutional binary risk transfer.
The answer is structural.
Not behavioral. Not informational.
The economics are here. The infrastructure is next.
Not elimination. Succession. Derivatives keep what they're built for. Binary risk moves.
Markets take the shape of their costs.