Tarek Mansour@mansourtarek_
The historical bottleneck for institutional risk transfer is liquidity.
The bottleneck for liquidity is having a price benchmark for each relevant risk (eg. WTI for oil).
Kalshi has built a large community of superforecasters who are the best in the world at pricing risk. This enables us to have a price benchmark for a much broader set of questions that people and institutions face.
Institutional adoption has started through ingesting these price benchmarks into traditional asset pricing model. While there is more work to be done, we're seeing a rapid expansion of data use-cases and integrations.
The next phase is using price benchmarks to offload risk through block trades and RFQ. This phase is in its early innings but it's starting to take shape.
It is hard to estimate the size of the market for risk transfer on non-traditional financial underlyings. The closest proxies are the re-insurance market and derivative desks at banks:
- re-insurance ~700B
- insurance-linked securities and parametric insurance (eg. cat bonds) ~$120-135B
- bank derivatives (structured products, dealer-to-dealer, exotics, etc.) ~200-400B
The current market is in the 1-1.5T range, but it's mostly illiquid and over-the-counter (OTC ie. you're trading against one counterparty).
Every time a major OTC market moved to exchange-traded, the market grew because a price benchmark got established, big-ask spreads collapsed, access stops being gated by Wall Street elites, and entirely new classes of participants enter: interest rate swaps (10-15x), equity options (20-30x), energy derivatives (5-8x).
The institutional use case for prediction markets could be a 10-15T market, with upside beyond that depending on how much they democratize access to products that are currently exclusive to Wall St.
