How do I handle correlated markets and avoid double-counting the same thesis?
Correlated markets move together because they share drivers (e.g., economic data affecting both rates and elections). If you hold multiple correlated positions, you may be taking much more risk than you realize.
Detailed Explanation
- Correlation in event markets: Different contracts can be linked by a common cause.
- Double-counting risk: You think you have diversification, but you’ve actually stacked the same bet.
- Practical approach: Treat correlated positions as one “risk bucket” and cap total exposure.
Common Scenarios
- Multiple contracts tied to the same macro indicator
- Election outcome markets correlated with policy passage markets
- Central bank decision markets correlated with inflation print markets
- Corporate approval markets correlated with trial results markets
Exceptions & Edge Cases
- If correlation is conditional (“only if X happens”), then model scenarios explicitly.
- If one market is a leading indicator for another, then you might trade the lag—not both.
- If one contract has cleaner settlement, then prefer it and reduce the rest.
Practical Examples
- You buy:
- “Rate cut by June” Yes at $0.45
- “Inflation below 3% by May” Yes at $0.55
Both depend on the same macro trend. A surprise inflation spike can hurt both simultaneously.
Actionable Takeaways
- ✅ Group positions by underlying driver (macro, policy, legal, etc.)
- ✅ Set a total risk cap per driver, not per market
- ✅ Prefer the contract with best liquidity + clearest settlement
- ✅ Use scenario thinking: “What single news item hurts all these?”
