Prediction Markets Fill Insurance Gaps with Risk Hedging Tools

Article by: G_Gyeomm

Compiled by: AIdidiaoJP, Foresight News

The recently launched AI risk management tool, Blanket, is attempting to transform prediction markets into practical insurance solutions for businesses. The logic is straightforward: businesses input their operational details, the system automatically identifies key risk exposures, and then recommends corresponding Kalshi event contracts to hedge those risks.

The mechanism of hedging itself is not complex. The contract structure of prediction markets is extremely clear—$1 if the event occurs, $0 if it does not. The real-time price of the contract reflects the market’s collective judgment of the probability of that event occurring.

It is precisely this streamlined structure that gives prediction markets the potential to become genuine hedging tools. Companies can purchase event contracts in advance that impact their operations—such as extreme weather, energy price fluctuations, or changes in tariff policies. If these risks materialize, the contract payouts can partially or fully offset operational losses.

For a concrete example: An ice cream shop might see its revenue drop by about $20,000 during a cool summer.

The hedging operation is as follows: purchase 20,000 temperature contracts at $0.30 per contract. If the average summer temperature falls below the preset threshold, each contract pays out $1. The total cost is $6,000.

There are only two possible outcomes:

• Cool summer: Temperatures fell below the threshold, resulting in $20,000 less in revenue, but the contract incurred a one-time loss of $20,000. The final net loss was locked in at $6,000—exactly the amount originally spent on the contract.
• Scorching summer: temperatures exceeded the threshold, revenue was unaffected, but the contract expired worthless, resulting in a total loss of $6,000.

Regardless of the outcome, the final loss is firmly capped at $6,000. This $6,000 is essentially the premium. And the rate isn’t set by an insurance company’s actuary or any traditional underwriter—it’s determined by the market itself, through real-time prices quoted by countless buyers and sellers using actual money.

The prediction market has accumulated sufficient speculative demand. It first gained recognition through election forecasting and successfully expanded into sports, largely resolving volume concerns. The industry generally believes that the next growth opportunity lies in expanding into more practical use cases, with hedging demand being repeatedly highlighted as the most promising direction.

In theory, its value is indeed significant. There is a vast gap unaddressed by existing hedging tools. Traditional commercial insurance policies for business interruption typically require physical damage as a prerequisite. For example, a ski shop that loses revenue due to a winter with little to no snowfall—this purely “business risk”—is almost impossible to insure with existing products on the market.

The futures market does offer mature hedging tools, but the barriers are high: you need to sign an ISDA agreement, open a dedicated futures account, post margin, and meet minimum contract size requirements. These conditions are not an issue for large institutions, but they are nearly impossible for ordinary small and medium-sized enterprises. Goldman Sachs can afford to maintain a dedicated derivatives trading team—obviously, the coffee shop on the corner cannot.

The problem lies in the persistent gap between theoretical soundness and practical application. Prediction markets have long been stigmatized as "gambling," and it has never been systematically verified whether they can truly function as independent hedging markets rather than merely speculative tools.

The real question to answer is: Have prediction markets actually been used for hedging? Does genuine hedging demand exist? Trading behavior itself can provide clues. We selected three sets of data for comparison.

The first group consists of CME grain futures—a classic traditional hedging market primarily used to offset losses from price fluctuations in agricultural and livestock products.

The second group is the Kalshi sports market—where hedging demand is extremely limited and trading is almost entirely speculation-driven.

The third group is the Kalshi weather market—which manages weather risk similarly to CME weather futures, while sharing the exact same event contract structure and trading environment as Kalshi’s sports markets. This makes it an ideal test case to determine whether its trading behavior aligns more closely with one side or the other.

Hedging and speculation typically exhibit different trading behaviors. Hedgers tend to establish positions before the risk window truly opens and hold them until expiration; speculators, on the other hand, trade more frequently, chasing price movements and exhibiting significantly higher turnover rates.

If Kalshi’s weather market turnover and positioning behavior are more similar to traditional hedging markets than to sports markets, then the hedging demand is genuine.

Conversely, if it offers no distinction from the sports betting market, real-world usage becomes more akin to pure speculation. In this case, tools like Blanket may merely respond to idealized industry visions rather than data-validated genuine demand.

This analysis covers 1,265 Kalshi markets settled between August 2025 and August 2026, filtered by a minimum cumulative volume of 500 contracts and trading activity lasting at least three days.

First, examine how frequently positions are traded across each market. Turnover rate is defined as daily trading volume divided by open interest (OI) on the same day. We calculated the daily turnover rate for each contract in every market and then took the median over the entire trading period.

The results are clear: Kalshi weather contracts had the lowest turnover at 0.210, traditional hedging product corn futures stood at 0.266, and Kalshi sports contracts had the highest turnover at 0.315.

The turnover rate of sports contracts is approximately 1.5 times that of weather contracts, suggesting that weather contracts have a relatively longer holding period, which preliminarily indicates the possible presence of genuine hedging demand.

However, be cautious: the turnover rate for corn futures sits in the middle, and the differences among the three data sets are not particularly large. Based solely on turnover rate, we cannot yet confirm the existence of hedging demand in the weather market. What this data clearly tells us for now is that weather contracts have significantly lower turnover than sports contracts.

The second key metric is the hold-to-maturity ratio—measuring how much open interest remains unchanged at settlement. It is calculated by dividing the final open interest for each contract by the cumulative trading volume. A higher value indicates that more positions were held firmly until expiration.

The difference in results is striking: regardless of trade duration, the hold-to-maturity ratio for weather contracts exceeded 0.5, whereas sports contracts stood at only 0.012 and 0.033, respectively. Over the 3- to 45-day trading window, weather contracts were 42.8 times higher than sports contracts; even beyond the 45-day window, the gap remained at 16.7 times.

This clearly shows that weather contracts are far more likely to be held until expiration than sports contracts. Hedgers hold these contracts not to profit from price fluctuations, but to receive payouts when specific risks materialize. Therefore, the high ratio of contracts held until expiration strongly supports the view that there is genuine hedging demand in the weather market.

Of course, this cannot be directly interpreted as the entire weather market being used for hedging. The data does not track the identities of buyers and sellers for individual positions, so it cannot be simply equated with the proportion of original buyers holding to expiration. What can currently be confirmed is that there is a clear distinction between the positioning behavior in weather contracts and sports contracts.

The last question is: When were these positions established? We divide the daily open interest for each contract by its peak open interest, then map the time from listing to expiration onto a 0% to 100% progress bar, and plot the median curve.

The criterion is based on when the position reaches half of its peak. If half the peak position is reached well before expiration, it indicates the position was established earlier—consistent with the behavior pattern of hedgers.

Weather contracts with a duration of 3 to 45 days reach half of their peak open interest at 47% of their lifecycle, leaving 53% of the time until expiration. In contrast, sports contracts in the same range reach half of their peak open interest at 65%, with only 36% of the time remaining until expiration.

Over contracts longer than 45 days, the differences are even more striking. Weather contracts reached half their duration at 32% remaining, while sports contracts had only 1.3% left. Across all timeframes, weather positions are established much earlier than sports positions.

This behavior of "early positioning" is a classic characteristic of traditional hedge markets. As of August 11, 2026, CME grain and livestock futures contracts with six months until expiration have accumulated substantial open interest. Even corn futures contracts expiring 16 months from now still hold 65,127 open positions.

This reflects a tendency to act well before the risk actually materializes. The behavior of Kalshi weather contracts is clearly more similar to traditional hedging markets than to sports markets.

To conclude: Kalshi’s weather market is neither a purely hedging market nor a purely speculative market like sports betting. While speculative demand still contributes a significant portion of liquidity, hedging demand has also become clearly apparent.

Three indicators point in the same direction: weather contracts are traded less frequently, more positions are held at settlement, and positions are established earlier. While a single indicator cannot confirm trading intent with certainty, the high consistency of these behaviors collectively supports the conclusion that there is indeed a distinct demand for positioning in Kalshi’s weather markets—substantially driven by genuine hedging needs, separate from those in sports markets.

More importantly, speculative demand is not so much a weakness of the market as it is a prerequisite for hedging functions to exist. A market with only hedgers and no speculators would struggle to find sufficient counterparty liquidity and sustained trading volume.

In prediction markets, speculators are responsible for pricing and providing liquidity, while hedgers transfer risks they wish to avoid onto this foundation. Risk is no longer directly underwritten by insurers but is naturally dispersed among market participants through trading.

Therefore, predicting the next phase of market growth isn't about pushing out speculation and fully shifting to hedging. What truly matters is how much real corporate hedging demand can be layered on top of the liquidity foundation already established by speculation. This is the key variable that determines whether it can evolve from a "fascinating speculative tool" into a "practical risk management infrastructure."