After Researching How to Add Leverage to Prediction Markets, I Found This Problem Almost Unsolvable

Odaily星球日报Publicado em 2025-12-17Última atualização em 2025-12-17

Resumo

Leverage in prediction markets remains a major unsolved challenge due to the unique nature of binary outcomes. Unlike traditional assets, prediction market shares have a fixed maximum value of $1 and can drop to zero instantly upon event resolution, causing discontinuous price "jumps." This jump risk makes high leverage extremely dangerous, as liquidation mechanisms fail during sudden price moves, potentially leaving lenders with losses. Real-world cases, like dYdX’s TRUMPWIN market during the 2024 election, show even well-designed systems with safeguards can fail under jump conditions. Current solutions vary: some platforms (e.g., HyperliquidX, Drift) limit leverage to 1-1.5x; others use dynamic risk management (e.g., D8X, PredictEX); while some offer high leverage without clear risk disclosure. The core issue is that prediction markets resemble exotic options, not perpetual futures, and may require specialized, exchange-level infrastructure—such as time-based margin calls and multi-layer insurance—to manage jump risk transparently on-chain. Until then, sustainable high leverage remains elusive.

This article is from:Nick-RZA

Compiled by | Odaily Planet Daily (@OdailyChina); Translator | Azuma (@azuma_eth)

Editor's Note: While organizing new projects emerging during the Solana Breakpoint period this week, I noticed some prediction markets featuring leverage functions are starting to appear. However, looking around the market, the current situation is: leading platforms generally steer clear of leverage functions; new platforms that claim to support leverage generally suffer from low multiples and small pools.

Compared to another hot track next door, Perp DEX, it seems the leverage potential in the prediction market track has not been effectively tapped. In the cryptocurrency market, which has an extremely high-risk appetite, this situation is extremely incongruous. To find the answer, I started collecting information and came across two very high-quality analytical articles during this period. One is a research report by Messari's Kaleb Rasmussen titled "Enabling Leverage on Prediction Markets," which is extremely well-argued but too long and math-heavy for easy translation; the other is "Everyone's Promising 20x Leverage on Prediction Markets. Here's Why It's Hard" by Linera's Nick-RZA, which is more concise and通俗, but sufficient to directly address the leverage难题 of prediction markets.

The following is the original text by Nick-RZA, compiled by Odaily Planet Daily.

Right now, almost everyone wants to add leverage to prediction markets.

Some time ago, I wrote an article titled "The Expression Problem" — the conclusion was that prediction markets limit the intensity of belief that capital can express. It turns out that many teams are already trying to solve this problem.

Polymarket, after receiving a $2 billion investment from the parent company of the New York Stock Exchange, has reached a valuation of $9 billion, and its founder Shayne Coplan appeared on "60 Minutes." Kalshi first raised $300 million at a $5 billion valuation, and then completed a new round of funding of $1 billion at an $11 billion valuation.

The track is heating up, and the players are racing to capture the next layer of demand — leverage. Currently, there are at least a dozen projects trying to build "prediction markets with leverage," some claiming to offer 10x, 20x, or even higher leverage. But when you actually study the analyses from teams seriously tackling this problem (like HIP-4, Drift's BET, Kalshi's framework) — you'll find their conclusions are converging on a very conservative number: between 1x and 1.5x.

This is a huge gap. So what's the problem?

Prediction Markets vs. Spot and Futures Trading

Let's start with the basics. Prediction markets allow you to bet on whether a specific event will happen: Will Bitcoin reach $150,000 by the end of the year? Will the 49ers win the Super Bowl? Will it rain in Tokyo tomorrow?

You buy a "share." If your prediction is correct, you get $1; if you're wrong, you get nothing. It's that simple.

If you think BTC will hit $150,000, and the "YES share" price is $0.40, you can spend $40 to buy 100 shares. If you're right, you get back $100, netting a $60 profit; if you're wrong, you lose the $40.

This mechanism gives prediction markets three distinct characteristics compared to spot trading or perpetual contracts:

First, there is a clear upper limit. The maximum value of a "YES share" (and conversely a "NO share") is always $1. If you buy at $0.90, the maximum upside is only 11%. This isn't like buying a meme coin early.
Second, the lower limit is truly zero. Not nearly zero after a bad crash, but literally zero. Your position doesn't decay slowly over time — it either succeeds or goes to zero.
Third, the outcome is binary, and the outcome confirmation is usually instantaneous. There is no gradual price discovery process. An election might be undecided one moment and the result announced the next. Correspondingly, the price doesn't slowly rise from $0.80 to $1.00; it "jumps" there.

The Nature of Leverage

The essence of leverage is borrowing money to amplify your bet.

If you have $100 and use 10x leverage, you are effectively controlling a $1000 position — if the price rises 10%, you don't make $10, you make $100; conversely, if the price falls 10%, you don't lose $10, you lose your entire principal. This is what liquidation means — the exchange will force-close your position before your losses exceed your principal to prevent the lender (the exchange or liquidity pool) from taking a loss.

Leverage works on conventional assets based on one key premise: the asset's price changes are continuous.

If you go long on BTC at $100,000 with 10x leverage, you would get liquidated around $91,000–$92,000, but BTC won't jump instantly from $100,000 to $80,000. It will only fall bit by bit, even if very quickly, it will be linear — 99500 → 99000 → 98400 ...... During this process, the liquidation engine will intervene in time and close your position. You might lose money, but the system is safe.

Prediction markets break this premise.

The Core Problem: Price Jumps

In derivatives, this is called "jump risk" or "gap risk"; the cryptocurrency community might call it "scam wicks."

Let's use the BTC example again. Suppose the price doesn't fall gradually but jumps directly — $100k one second, $80k the next, with no intermediate traded prices, no $99k, no $95k, and certainly no $91k where you could be liquidated.

In this case, the liquidation engine still tries to close the position at $91k, but that price never exists in the market; the next available price is directly at $80k. At this point, your position is not just liquidated; it is deeply underwater, and this loss must be borne by someone.

This is exactly the situation prediction markets face.

When election results are announced, game outcomes are decided, or major news breaks, prices don't move slowly and linearly; they jump directly. Furthermore, leveraged positions within the system cannot be effectively unwound because there is simply no liquidity in between.

Messari's Kaleb Rasmussen wrote a detailed analysis on this issue (Odaily highly recommends reading: https://messari.io/report/enabling-leverage-on-prediction-markets). His final conclusion was: If capital lenders can correctly price jump risk, the fees they need to charge (similar to funding rates) should eat up all the upside gains of the leveraged position. This means that for traders, opening a leveraged position while paying a fair fee offers no advantage over building a position directly without leverage, and it also carries greater downside risk.

So, when you see a platform claiming to offer 10x, 20x leverage on prediction markets, there are only two possibilities:

Either their fees do not correctly reflect the risk (meaning someone is bearing uncompensated risk);
Or the platform uses some undisclosed mechanism.

A Real Case Study: dYdX's Lesson Learned the Hard Way

This is not just theoretical; we already have a real case.

In October 2024, dYdX launched TRUMPWIN — a leveraged perpetual market on whether Trump would win the election, supporting up to 20x leverage, with the price oracle coming from Polymarket.

They were not unaware of the risks and had even designed multiple protective mechanisms for the system:

Market makers could hedge their dYdX exposure on Polymarket's spot market;
There was an insurance fund to cover losses when liquidations couldn't proceed smoothly;
If the insurance fund was depleted, losses would be socialized among all profitable traders (unpopular, but better than system bankruptcy; a crueler version is ADL, directly closing winners' positions);
A dynamic margin mechanism would automatically reduce available leverage as open interest increased.

By perpetual contract standards, this was quite mature. dYdX even publicly issued warnings about deleveraging risks. Then, election night came.

As the results became clear and a Trump victory seemed almost certain, the price of the "YES share" on Polymarket jumped from around $0.60 directly to $1.00 — not gradually, but in a jump. This jump breached the system.

The system tried to liquidate underwater positions, but there simply wasn't enough liquidity; the order book was thin; market makers who were supposed to hedge on Polymarket couldn't adjust their positions fast enough; the insurance fund was breached... When positions couldn't be liquidated smoothly, random deleveraging kicked in — the system forcibly closed some positions, regardless of whether they had sufficient collateral.

According to the analysis by Kalshi's Crypto Lead John Wang: "Hedging delays, extreme slippage, and liquidity evaporation caused losses for traders who should have been executable." Some traders who should have been safe — with correct positions and sufficient collateral — still suffered losses.

This wasn't some unregulated garbage DEX; it was once one of the largest decentralized derivative exchanges globally, with multiple layers of protection and clear warnings issued in advance.

Even so, its system partially failed under real market conditions.

Solutions Proposed by the Industry

Regarding the leverage problem in prediction markets, the entire industry has split into three camps, and this division itself reveals each team's attitude towards risk.

Camp 1: Limit Leverage

Some teams, after seeing the mathematical reality, have chosen the most honest answer — offer almost no leverage.

HyperliquidX's HIP-4 proposal sets the leverage cap at 1x — not because it's technically impossible, but because they believe it's the only safe level for binary outcomes.
DriftProtocol's BET product requires 100% margin, meaning full collateralization, no borrowing.
The framework published by Kalshi's Crypto Lead John Wang also suggests that safe leverage is around 1–1.5x without additional protective mechanisms.

Camp 2: Engineer Against the Risk

Another set of teams is trying to build sufficiently complex systems to manage the risk.

D8X dynamically adjusts leverage, fees, and slippage based on market conditions — stricter restrictions as it gets closer to settlement or extreme probabilities;
dYdX built the protective mechanisms we just saw fail on election night and is still iterating;
PredictEX's solution is to increase fees and reduce maximum leverage when price jump risk rises, relaxing them when the market calms down — its founder Ben put it bluntly: "If you directly apply the perpetual contract model, market makers will be completely wiped out in the second the probability jumps from 10% to 99%."

These engineering teams are not claiming to have solved the problem; they are trying to manage risk in real-time.

Camp 3: Launch First, Fix Later

Some teams choose to launch quickly, directly claiming 10x, 20x, or even higher leverage, without publicly disclosing how they handle jump risk. Maybe they have an elegant solution not yet public, maybe they want to learn in a production environment.

The crypto industry has a tradition of "move fast and加固 later." The market will ultimately test which approach can stand.

What Will Happen in the Future?

We are facing a problem 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 suggested some possible directions:

Don't price the risk for the entire position at once, but charge rolling fees based on changing conditions;
Design auction mechanisms for price jumps to return value to liquidity providers;
Build systems that allow market makers to profit consistently without being crushed by informational advantages.

But these solutions are essentially improvements on the existing architecture.

Deepanshu from EthosX proposed a more fundamental reflection; he previously researched and built clearing infrastructure like LCH, CME, and Eurex in JPMorgan's global clearing business. In his view, trying to add leverage to prediction markets using the perpetual contract model is itself solving the wrong problem.

Prediction markets are not perpetual contracts; they are extreme exotic options — more complex than the products traditionally handled in traditional finance. And exotic options aren't traded on perpetual exchanges; they are typically settled through specialized clearing infrastructure designed for their risks. Such infrastructure should be able to:

Give traders a time window to respond to margin calls;
Have a transfer mechanism allowing other traders to take over positions before they go失控;
Multi-tiered insurance funds, making it clear to participants that tail risks are socialized.

This isn't new — clearing houses have been managing jump risk for decades. The real challenge is — how to achieve all this on-chain, transparently, and at the speed required by prediction markets.

Dynamic fees and leverage decay are just the beginning. The team that ultimately solves the problem will likely not just build a better perpetual engine, but a "clearing house level" system. The infrastructure layer remains unsolved, and market demand is already very clear.

Perguntas relacionadas

QWhat is the core challenge of adding leverage to prediction markets according to the article?

AThe core challenge is 'jump risk' or 'gap risk'. Unlike traditional assets where price changes are continuous, prediction market outcomes are binary and their prices can jump instantly from one value to $1 or $0. This makes it impossible for liquidation engines to function properly during these jumps, leading to undercollateralized positions that someone must absorb the losses for.

QWhat was the real-world example provided in the article that demonstrated the failure of a leveraged prediction market system?

AThe real-world example was dYdX's TRUMPWIN market during the 2024 US presidential election. Despite having multiple protective mechanisms (market maker hedging, an insurance fund, loss socialization, dynamic margins), the price of 'YES shares' jumped from around $0.60 to $1.00 instantly. This jump overwhelmed the system, causing failed liquidations, the insurance fund to be depleted, and resulting in losses for some traders who should have been safe, via a process of random deleveraging.

QWhat are the three different approaches (camps) that teams are taking to address the leverage problem?

A1. Limiting Leverage: Teams like HyperliquidX, DriftProtocol, and Kalshi advocate for very low (1x to 1.5x) or no leverage, deeming it the only safe approach for binary outcomes. 2. Engineering Against Risk: Teams like D8X, dYdX, and PredictEX build complex systems with dynamic leverage adjustments, fees, and risk management mechanisms to try and mitigate jump risk in real-time. 3. 'Move Fast and Fix Later': Some teams launch with claims of high leverage (10x, 20x) without fully disclosing their risk management solutions, opting to learn and adapt in a live market environment.

QAccording to the analysis from Messari's Kaleb Rasmussen, what is the implication if lenders correctly price the jump risk?

AIf lenders correctly price the jump risk, the fees they would need to charge (similar to funding rates) would be so high that they would 'eat up all the upside of the leveraged position.' This means that for a trader, using leverage would offer no profit advantage over a simple unleveraged position after paying the fair fee, while simultaneously exposing them to greater downside risk.

QWhat fundamental reframing of the problem does EthosX's Deepanshu propose, and what kind of system does he suggest is needed?

ADeepanshu reframes the problem by stating that prediction markets are not perpetual contracts but are instead 'extreme exotic options.' He argues that trying to use a perpetual contract model is solving the wrong problem. The solution requires building a 'clearinghouse-grade' system on-chain, which would include features like time windows for margin calls, mechanisms for transferring risky positions, and multi-layered insurance funds that explicitly socialize tail risk, similar to traditional financial infrastructure for complex derivatives.

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