The real challenge is how to achieve all of this on-chain, transparently, and at the speed required by prediction markets.
Author: hyperreal_nick
Source: Odaily Planet Daily
This week, while organizing new projects that emerged during the Solana Breakpoint cycle, I noticed that some prediction markets focusing on leverage features are surfacing. However, looking around the market, the current situation is that leading platforms generally shy away from leverage features; new platforms claiming to support these features often face issues such as lower multiples and smaller pools.
Compared to the neighboring hot track Perp DEX, it seems that the leverage space in the prediction market track has not been effectively explored. In the highly risk-tolerant cryptocurrency market, this situation is extremely discordant. To address this, I began collecting information to seek answers, during which I came across two quite high-quality analytical articles. One is a research report by Kaleb Rasmussen from Messari on this issue (Enabling Leverage on Prediction Markets), which provides a very thorough argument but is too lengthy and contains too many mathematical calculations to be conveniently translated; the other is by Nick-RZA from Linera (Everyone's Promising 20x Leverage on Prediction Markets. Here's Why It's Hard), which is more concise and accessible but sufficiently addresses the leverage challenges in prediction markets.
The following is the original content from Nick-RZA, translated by Odaily Planet Daily.
Currently, almost everyone wants to add leverage functionality to prediction markets.
Earlier, I wrote an article titled (expression problem)—the conclusion is that prediction markets limit the strength of beliefs that capital can express. It turned out that many teams are already attempting to solve this issue.
Polymarket's valuation reached $9 billion after its parent company invested $2 billion on the New York Stock Exchange, and its founder Shayne Coplan appeared on (60 Minutes). Kalshi initially raised $300 million at a $5 billion valuation, then completed a new round of funding at a $11 billion valuation.
The track is heating up, and competitors are vying for the next layer of demand—leverage. Currently, there are at least a dozen projects trying to build 'leveraged prediction markets', some claiming to achieve 10x, 20x, or even higher, but when you truly study the analyses provided by teams seriously addressing this issue (like HIP-4, Drift's BET, Kalshi's framework)—you will find their conclusions converge on a very conservative number: between 1x and 1.5x.
There is a significant gap; what is the problem?
Prediction markets vs. spot and contract trading
Let’s start with the basics. Prediction markets allow you to bet on whether a certain event will happen: Will Bitcoin rise to $150,000 by the end of the year? Will the 49ers win the Super Bowl? Will it rain in Tokyo tomorrow?
What you are buying is a 'share'; if you predict correctly, you will receive $1; if you are wrong, you get nothing—it's that simple.
If you believe BTC will rise to $150,000 and the price of 'YES shares' is $0.40, you can spend $40 to buy 100 shares. If you're right, you'll get back $100, netting a profit of $60; if you're wrong, the $40 is lost.
This mechanism brings three characteristics to prediction markets that are completely different from spot trading or perpetual contracts:
· First, there is a clear upper limit. The highest value of 'YES shares' (similarly for 'NO shares') is always $1. If you buy in at $0.90, the maximum upside is only 11%. This is not like buying a meme coin early.
· Second, the lower limit is a true zero. It is not a near-zero that has fallen drastically, but a literal zero. Your position will not gradually become uneducated over time—either the prediction is successful, or it goes to zero.
· Third, the results are binary, and the confirmation of results is usually instantaneous. There is no gradual price discovery process here; an election may be undecided one moment and immediately announced the next. Correspondingly, prices do not rise gradually from $0.80 to $1, but jump directly over.
The essence of leverage
The essence of leverage is borrowing money to amplify your bets.
If you have $100 and use 10x leverage, you are actually controlling a $1000 position—if the price rises by 10%, you earn not $10, but $100; conversely, if the price drops by 10%, you lose not $10, but your entire principal. This is also the meaning of liquidation—trading platforms will forcibly close your positions before you lose more than your principal to prevent the lender (the trading platform or liquidity pool) from bearing the loss.
Leverage can only exist on conventional assets under a key premise: the price changes of the asset are continuous.
If you go long BTC with 10x leverage at $100,000, you would likely face liquidation around $91,000 to $92,000, but BTC will not instantaneously move from $100,000 to $80,000. It will only decline gradually, even if very quickly, it will be linear—$99,500 → $99,000 → $98,400... During this process, the liquidation engine will intervene at the right time and close your position. You might lose money, but the system is safe.
Prediction markets have jumped out of this premise.
Core issue: price jumps
In the derivatives space, this is referred to as 'jump risk' or 'gap risk', while the crypto community might call it 'scam wicks'.
Let's use the BTC example. Suppose the price does not decrease gradually but jumps directly—$100,000 one second, $80,000 the next, with no intermediate prices, no $99,500, no $99,000, and certainly no $91,000 where you could be liquidated.
In this case, the liquidation engine still tries to close positions at $91,000, but that price does not exist in the market, and the next executable price goes straight to $80,000. At this point, your position is not only liquidated but is deeply underwater, and this portion of the loss must be borne by someone.
This is exactly the situation faced by prediction markets.
When election results are announced, match outcomes are determined, or significant news breaks, prices do not move linearly; they jump directly. Furthermore, positions with leverage within the system cannot be effectively unwound because there is simply no liquidity in between.
Messari's Kaleb Rasmussen has written a detailed analysis on this issue (https://messari.io/report/enabling-leverage-on-prediction-markets). His final conclusion is that if lenders can accurately price jump risks, the fees they need to charge (similar to capital fees) should consume all the upside gains of leveraged positions. This means that for traders, opening leveraged positions at fair rates offers no advantage over directly opening positions without leverage, and they must also bear greater downside risks.
So, when you see a platform claiming to offer 10x, 20x leverage in prediction markets, there are only two possibilities:
· Either their fees do not accurately reflect the risk (which means someone is bearing uncompensated risk);
· Either the platform has used some undisclosed mechanism.
Real case: the pitfalls of dYdX
This is not just theoretical; we already have real cases.
In October 2024, dYdX launched TRUMPWIN—a leveraged perpetual market on whether Trump would win the election, supporting up to 20x leverage with price oracle sourced from Polymarket.
They are not unaware of the risks; they even designed multiple protective mechanisms for the system:
· Market makers can hedge their exposure to dYdX in the spot market on Polymarket;
· An insurance fund is set up to cover losses when liquidation cannot be smoothly executed;
· If the insurance fund is exhausted, losses will be shared among all profitable traders (though no one likes this, it's better than the system going bankrupt; a harsher version is ADL, which directly liquidates the winners' positions);
· Dynamic margin mechanisms will automatically reduce available leverage as open contracts increase.
Under the standards of perpetual contracts, this is already quite mature. dYdX even publicly issued warnings about de-leveraging risks. Then, election night came.
As the results became clearer, Trump's victory was almost certain, the price of 'YES shares' on Polymarket jumped from about $0.60 to $1—not gradually, but in a leap that broke the system.
The system attempted to liquidate underwater positions but lacked sufficient liquidity, the order book was thin; market makers who should have hedged on Polymarket couldn't adjust their positions in time; the insurance fund was also breached... When positions cannot be liquidated smoothly, random de-leveraging was triggered—the system forcibly closed some positions regardless of whether the counterparty had sufficient collateral.
According to analysis by Kalshi's crypto head John Wang: 'Hedging delays, extreme slippage, and liquidity evaporation caused originally executable traders to suffer losses.' Some traders who should have been safe—correct positions, sufficient collateral—still incurred losses.
This is not a garbage DEX without risk control, but rather one of the largest decentralized derivatives trading platforms in the world, equipped with multiple layers of protective mechanisms, and it issued clear warnings in advance.
Even so, the system has still shown some failures in real market environments.
Industry-provided solutions
Regarding leverage issues in prediction markets, the entire industry has diversified into three camps, and this diversification itself reveals the attitudes of various teams toward risk.
Camp One: Restrict leverage
Some teams chose the most honest answer after seeing the mathematical reality—offering almost no leverage.
· HyperliquidX's HIP-4 proposal sets the leverage cap at 1x—not because technology cannot achieve more, but because it believes this is the only safe level under binary outcomes.
· DriftProtocol's BET product requires 100% collateral, meaning full collateralization, no borrowing.
· Kalshi's crypto head John Wang's framework also suggests that without additional protective mechanisms, safe leverage is around 1–1.5x.
Camp Two: Using engineering to combat risk
Another part of the team is trying to build a sufficiently complex system to manage risk.
· D8X will dynamically adjust leverage, fees, and slippage based on market conditions—the closer to settlement or extreme probabilities, the stricter the limits;
· dYdX built the protective mechanisms we just saw fail on election night, and continues to iterate;
· PredictEX's solution is to raise fees and lower maximum leverage when jump risk increases, and to relax these restrictions when the market stabilizes—its founder Ben puts it bluntly: 'If we directly apply the perpetual contract model, market makers will be completely wiped out in a second when the probability jumps from 10% to 99%.'
These engineering-focused teams do not claim to have solved the problem; they are just trying to manage risk in real-time.
Camp Three: Go ahead and then fill in later
Some teams choose to launch quickly, directly claiming 10x, 20x, or even higher leverage, without disclosing how they handle jump risks. Perhaps they have an elegant solution that has not yet been made public, or maybe they want to learn in a production environment.
The crypto industry has a tradition of 'running first and solidifying later'; the market will ultimately test which approach can stand.
What will happen in the future?
We are facing a question with an extremely open design space, which is what makes it most interesting.
Kaleb Rasmussen's Messari report not only diagnosed the problem but also proposed some possible directions:
· Do not price risk for the entire position at once, but charge rolling fees based on changing conditions;
· Design an auction mechanism for price jumps that returns value to liquidity providers;
· Build a system that allows market makers to continue profiting without being crushed by information advantages.
But these solutions are essentially improvements within the existing framework.
Deepanshu of EthosX proposed a more fundamental reflection, having studied and built clearing infrastructures like LCH, CME, Eurex in JPMorgan's global clearing business. He believes that trying to apply a perpetual contract model to leverage prediction markets is fundamentally solving the wrong problem.
Prediction markets are not perpetual contracts but are extreme exotic options—more complex than the products typically handled in traditional finance. Exotic options are not traded on perpetual trading platforms; they are generally settled through clearing infrastructures designed specifically for their risks. Such infrastructure should be able to:
· Give traders a time window to respond to margin calls;
· A mechanism allowing other traders to take over positions before they spiral out of control;
· Multi-tiered insurance funds to socialize the acceptance of tail risks among participants.
These are not new—clearing houses have been managing jump risks for decades. The real challenge is how to achieve all of this on-chain, transparently, at the speed required by prediction markets.
Dynamic fees and leverage decay are just the starting point; the team that can truly solve the problem will likely not only create a better perpetual engine but will also build a 'clearing house-level' system. The infrastructure layer remains unresolved, while market demand is already very clear.
