Blanket, a newly launched AI risk management tool, is attempting to turn prediction markets into a practical insurance-style instrument for businesses. A company enters information about its operations, the system identifies key risk exposures, and then recommends relevant event contracts on Kalshi as a hedge.
A market-priced form of insurance
The model relies on the basic structure of a prediction market contract: if the event happens, the contract pays $1; if it does not, it pays $0. The live price reflects the market’s collective estimate of the event’s probability.
That simplicity is what gives prediction markets a possible role in risk management. A business can buy contracts tied to events that could affect its operations, such as abnormal weather, swings in energy prices, or changes in tariff policy. If the risk materializes, the payout can offset part or even all of the operating loss.
The article gives a concrete example. An ice cream shop could lose about $20,000 in revenue during a cool summer. To hedge that exposure, it could buy 20,000 temperature contracts at $0.30 each. If the average summer temperature falls below a preset threshold, each contract pays $1. The total cost is $6,000.
- In a cool summer, the shop loses $20,000 in revenue, but the contracts pay out $20,000. The net loss is capped at $6,000.
- In a hot summer, revenue is unaffected, but the contracts expire worthless, and the $6,000 premium is lost.
Either way, the maximum loss is fixed at $6,000. The article treats that amount as an insurance premium. The difference is that the premium is not set by actuaries or a traditional underwriter. It is set by the market itself, in real time, through trading.
Is hedging in prediction markets actually happening?
Prediction markets first built demand through election contracts and later expanded into sports, where trading volume is no longer the central issue. In the article’s framing, the next growth phase depends on real-world use cases, and hedging is one of the most frequently cited possibilities.
The appeal is clear. There are large areas of commercial risk that existing hedging tools do not cover well. Traditional business interruption insurance usually requires physical damage as a trigger. If a ski shop loses revenue because an entire winter brings little snow, that kind of pure operating risk is difficult to insure through standard products.
Futures markets do offer mature hedging tools, but access comes with meaningful friction: ISDA agreements, dedicated futures accounts, margin requirements, and minimum contract sizes. Large institutions can absorb that complexity. Small and medium-sized businesses often cannot. The article contrasts a firm like Goldman Sachs with a neighborhood coffee shop to make that point.
Still, a plausible theory is not the same thing as proven usage. Prediction markets have long carried a gambling label, and whether they can function as an independent hedging market rather than a speculative venue has not been systematically verified.
The article focuses on a narrower question: are prediction markets actually being used to hedge, and is there real hedging demand in the data? To test that, it compares three groups:
- CME grain futures, a conventional hedging market used to manage price swings in agricultural and livestock products.
- Kalshi sports markets, where hedging demand is assumed to be very limited and speculation dominates trading.
- Kalshi weather markets, which address weather risk in a way similar to CME weather futures while sharing the same event-contract structure and trading environment as Kalshi sports markets.
That makes Kalshi weather a useful test case. The question is whether its trading behavior looks more like a traditional hedge market or more like a speculative sports market.
Hedgers and speculators tend to behave differently. Hedgers usually establish positions before the risk window arrives and hold them until expiry. Speculators trade in and out more actively, chasing price moves and generating higher turnover. If Kalshi weather markets look closer to traditional hedging markets than to sports markets on turnover and position behavior, that would point to genuine hedging demand. If not, tools like Blanket may be responding more to industry hope than to demonstrated use.
The dataset used in the analysis covers 1,265 Kalshi markets settling between August 2025 and August 2026. The filter required at least 500 contracts in cumulative volume and at least three days of trading.
Data point one: average daily turnover
The first metric is average daily turnover, defined as daily trading volume divided by open interest. The article calculates a daily turnover rate for each contract in each market and then takes the median across the contract’s trading life.
The result is straightforward. Kalshi weather contracts have the lowest turnover at 0.210. Corn futures, a traditional hedging product, come in at 0.266. Kalshi sports contracts are the highest at 0.315.
On that measure, sports contracts turn over about 1.5 times faster than weather contracts. That suggests a greater tendency to hold weather positions for longer periods, an early sign of actual hedging use.
The article is careful not to overstate the point. Corn futures sit between the two, and the gap across the three groups is not overwhelming. Turnover alone does not prove hedging demand. What it does show is that weather contracts trade meaningfully less often than sports contracts.
Data point two: hold-to-expiry ratio
The second metric is the hold-to-expiry ratio, calculated as final open interest divided by cumulative volume for each contract. The higher the number, the more positions remain in place at settlement rather than being churned through repeated trading.
Here the difference is much sharper. Regardless of contract duration, weather contracts show a hold-to-expiry ratio above 0.5. Sports contracts come in at just 0.012 and 0.033.
In markets lasting 3 to 45 days, the weather figure is 42.8 times that of sports. In contracts lasting more than 45 days, the gap is still 16.7 times.
The article argues that weather contracts are far more likely to be bought and then left in place. That fits a hedging motive: the holder wants the payout if the risk occurs, not short-term price gains from active trading. A high hold-to-expiry ratio, in that reading, strongly supports the presence of real hedging demand in weather markets.
It also adds an important limitation. The data does not identify the counterparties behind each position, so the ratio cannot be read as the share of original buyers who held until settlement. What can be said is narrower: weather and sports contracts exhibit materially different holding behavior.
Data point three: when positions are built
The third test asks when positions are established. The analysis divides each contract’s daily open interest by its peak open interest, then maps the time from listing to expiry onto a 0% to 100% lifecycle and plots a median curve.
The key reference point is when a contract reaches half of peak open interest. If that happens earlier, with more time left before expiry, the position is being built sooner, which is more consistent with hedging behavior.
For weather contracts lasting 3 to 45 days, half of peak open interest is reached at 47% of the contract lifecycle, leaving 53% of the time still to run. In the same duration bucket, sports contracts do not hit that point until 65% of the lifecycle, with only 36% of the time remaining.
For contracts lasting more than 45 days, the gap is even wider. Weather contracts reach the halfway mark with 32% of the time left before expiry. Sports contracts do so with just 1.3% remaining. In both buckets, weather positions are built much earlier than sports positions.
The article treats that pattern of early positioning as a hallmark of traditional hedging markets. As a comparison, it notes that as of Aug. 11, 2026, CME grain and livestock futures already had substantial open interest in contracts still six months from expiry. Corn futures even carried 65,127 positions in contracts that would not expire for another 16 months.
On that basis, the article says the behavior of Kalshi weather contracts looks much closer to a conventional hedging market than to a sports speculation market.
Speculation supplies the liquidity that hedging needs
The article’s conclusion is not that Kalshi weather markets are purely for hedging, nor that they resemble purely speculative sports markets. Instead, it argues that speculative demand still provides a meaningful share of liquidity, while hedging demand has also become visible on top of that base.
All three indicators point in the same direction: weather contracts trade less frequently, retain more open interest into settlement, and build positions earlier. No single metric can identify intent with complete certainty, but the consistency across those behaviors supports the view that Kalshi weather markets contain a form of position demand that differs from sports markets, and that a significant part of it is likely genuine hedging demand.
The broader point is that speculation is not a flaw to be removed before prediction markets can become useful for risk management. A market made up only of hedgers, with no speculators, would struggle to find counterparties and maintain liquidity.
In this structure, speculators handle price discovery and supply liquidity. Hedgers use that market to transfer risks they do not want to carry. Risk is no longer underwritten directly by an insurance company; it is dispersed across market participants through trading.
So the next phase of growth for prediction markets, in the article’s view, does not depend on pushing speculation out and turning fully to hedging. The real question is how much genuine corporate hedging demand can be layered on top of the liquidity that speculation has already built. That is the variable it sees as decisive in determining whether prediction markets can move from an interesting speculative tool to workable risk-management infrastructure.