Conditional prediction markets: building complex bets on-chain

Single-event prediction markets answer simple questions. Will X happen? Yes or no. Conditional markets answer complex questions. Will X happen given that Y happens? What's the joint probability of X and Y? This progression unlocks financial instruments that traditional markets cannot easily create.

What conditional markets enable

Conditional markets separate joint probabilities into their components.

Consider two events: an election outcome and a Federal Reserve decision. Standard prediction markets tell you the probability of each event independently. Conditional markets tell you how they relate.

The price of "Rate cut given Candidate A wins" reveals market expectations about policy under different political scenarios. Comparing to "Rate cut given Candidate B wins" surfaces expected policy divergence.

This information doesn't exist in traditional markets. Options on interest rates don't cleanly separate political scenarios. Polling doesn't price conditional economic expectations.

Conditional markets also enable new trading strategies:

Correlation trades express views on relationships between events. If you believe two events are more correlated than markets imply, you can construct positions that profit from that correlation.

Scenario analysis trades let you bet on specific future states. Rather than just "rates go down," you can bet on "rates go down in the scenario where unemployment rises."

Conditional hedges offset exposure only in specific scenarios. A company worried about regulatory changes under one political outcome can hedge that specific scenario without paying for protection in all scenarios.

How conditional pricing works

Conditional probabilities follow mathematical relationships that create pricing constraints.

If P(A and B) is the probability both events occur, and P(A) is the probability A occurs, then P(B|A) = P(A and B) / P(A).

Translation: the conditional probability of B given A equals the joint probability divided by A's probability.

Markets must satisfy this relationship or arbitrage opportunities exist.

Suppose the market prices: P(A) = 0.60, P(A and B) = 0.30, P(B|A) = 0.40.

Check: 0.30 / 0.60 = 0.50, not 0.40. The conditional price is too low. Buying P(B|A) and selling appropriately weighted positions in P(A and B) and P(A) creates risk-free profit.

Arbitrageurs enforce these relationships, keeping conditional prices internally consistent with unconditional prices.

In practice, transaction costs, liquidity constraints, and timing mismatches allow some divergence. But major mispricings get corrected quickly in liquid markets.

Protocol mechanics for composability

Building conditional markets on-chain requires specific protocol designs.

Outcome token splitting creates the base layer. A single "Election" market creates outcome tokens for each candidate. A single "Fed Decision" market creates rate tokens.

Conditional token generation combines base tokens. Gnosis conditional token framework lets you split any token conditionally. An "Election-A" token can be split into "Election-A and Rate-Cut" plus "Election-A and No-Rate-Cut" tokens.

This splitting can nest arbitrarily deep. Three-way conditional tokens. Four-way combinations. The protocol mechanics support arbitrary complexity.

Resolution cascades handle settlement. When the election resolves, conditional tokens on that election resolve simultaneously. "Election-A and Rate-Cut" becomes just "Rate-Cut" if A wins, or becomes worthless if A loses.

The mechanics enable programmable financial instruments without bespoke contract creation. Anyone can create conditional positions by splitting existing outcome tokens.

DeFi primitives meet prediction markets

Prediction market shares are tokens. Tokens can interact with DeFi protocols. This composability creates new financial instruments.

Collateralization

Prediction market positions can collateralize other positions.

A highly confident position (say, 95% probability outcome) has expected value near $0.95 per share. Lending protocols could accept this as collateral, lending perhaps $0.80 per share.

This increases capital efficiency. Rather than locking funds in prediction markets unable to be used elsewhere, traders can maintain exposure while deploying collateral value.

Risks include resolution uncertainty. If the outcome unexpectedly reverses, collateral value goes to zero, potentially causing liquidations. Protocols accepting prediction market collateral must price this discontinuous risk.

Lending markets

Borrowing and lending prediction market shares enables new strategies.

Short selling becomes possible. Borrow outcome shares, sell them, buy back cheaper after prices fall. Standard market making technique now applies to prediction markets.

Leveraged exposure emerges. Borrow against one position to buy more. Amplify returns (and losses) on probability moves.

Interest rates on borrowed prediction market shares reveal carrying costs and sentiment. High borrow demand indicates short selling interest.

Index products

Collections of prediction market positions can be packaged into index-like instruments.

An "Election Basket" might combine multiple race outcomes into single exposure. A "Macro Scenario Index" might weight positions across Fed decisions, employment reports, and GDP releases.

Passive prediction market exposure becomes possible through index products. Rather than actively trading individual markets, investors hold diversified probability portfolios.

Financial market implications

Composable prediction markets create instruments traditional finance cannot easily replicate.

Correlation trading without correlation swaps. Traditional markets express correlation views through variance swaps, dispersion trades, and complex derivatives. Conditional prediction markets express correlations directly through conditional price differences.

Event risk hedging becomes granular. CFOs currently hedge currency risk, interest rate risk, and commodity risk separately. Conditional prediction markets could enable hedging specific scenario combinations: currency exposure given a specific election outcome, for instance.

Arbitrage across market types emerges. If conditional prediction markets and traditional derivatives both imply correlations, divergences create arbitrage. Traders can buy implied correlation cheap in one market and sell it expensive in another.

Structured product innovation accelerates. Traditional structured products require months of legal documentation and counterparty negotiation. On-chain composability enables permissionless structured product creation.

Economic implications

Composability has broader systemic effects.

Capital efficiency increases. Assets previously locked in single-use positions become productive collateral. The same dollar supports multiple economic functions simultaneously. This efficiency gain reflects genuine value creation.

Systemic risk expands. When prediction market positions collateralize DeFi lending which funds other prediction market positions, contagion pathways multiply.

A surprise election outcome could cascade through collateral liquidations, lending market stress, and forced selling in related prediction markets. Interconnection creates fragility alongside efficiency.

Complexity creates opacity. As conditional structures nest deeper and interconnections multiply, understanding systemic exposure becomes harder. Risk may accumulate in places that aren't obvious until crisis reveals them.

Information production scales. Conditional markets create demand for conditional probability estimates. Analysts who can price correlations and scenario probabilities become valuable. Information markets deepen.

Risks of interconnection

Composability brings risks alongside benefits.

Oracle risk compounds. Conditional markets inherit oracle risk from all underlying markets. A three-way conditional inherits risk from three separate resolution processes. Single oracle failures cascade through all dependent instruments.

Liquidity fragmentation occurs. Each conditional combination is a separate market. Liquidity that would concentrate in simple markets spreads across many conditional variations. Most conditional markets have thin liquidity.

Complexity exceeds understanding. Traders may hold conditional positions without fully comprehending their risk profiles. Unexpected correlations at resolution can produce surprising losses.

Smart contract risk multiplies. Each layer of composability adds smart contract dependencies. Bugs in conditional token contracts, in lending protocols that accept them, or in any intermediate layer can produce losses.

Regulatory ambiguity intensifies. If simple prediction markets face regulatory questions, complex composable prediction market derivatives face more. Structured products combining prediction markets with lending and leverage attract regulatory attention.

Related Concepts

• Prediction market mechanics: How basic prediction markets work
• Oracle risk: Resolution failure and its consequences
• Prediction market fees: Who captures value from trading
• DeFi business models: Understanding protocol economics