Parlays on HIP-4

Published: at 03:23 PM

Written by: rajiv (framework), kratik, krane (asula), marc (felix)

Thanks to Jim and Benedict for their thoughtful comments and feedback.

HIP-4 brought forward a community proposal for event futures. The goal of the proposal was somewhat simple: expand the scope and shape of markets that can be deployed to Hyperliquid permissionlessly. In this post, we explore how parlays, a popular sports betting product, can be built on top of event futures.

Demand for non linear payouts

People love the possibility of turning a little bit of money into a lot of money. The most common expression of this in crypto is the use of leverage with perps. As designed, HIP-4 is constrained to 1x margin disallowing any leverage. Moreover, in prediction markets, allowing leverage doesn’t create infinite upside (there’s no space above 1), but it still shrinks the distance to liquidation and makes users vulnerable to instantaneous jump in pricing (news, oracle ticks, resolution). For example, during resolution, there’s likely no liquidity to take on distressed positions before the price gaps to 0 or 1, leaving leveraged accounts with bad debt that the platform needs to eat. This is because no liquidator wants to take on a leveraged position at 0.2 if they already know the resolution will take the market down to 0. Isolated margin 1x eliminates these vulnerabilities and keeps the market structure simple and fair. However, this also makes for a less interesting and fun financial product. This is where Parlays come in.

Parlays are a cleaner substitute for ‘leverage’ in prediction markets because they scale returns by combining edges as opposed to borrowing risk. Instead of taking a single binary bet and cranking up exposure, you place several 1× bets and link them together via an AND operator; the payout grows when all legs resolve to true, and boost upside in proportion to how confident (and independent) the user’s views are. In short, Parlays help traders compound their edge.

Moreover, parlays are path independent, unlike leverage. Volatile odds cannot ‘liquidate’ users. This is not just important from a payoff perspective, but it’s also super important from a UX perspective if non-professional traders are to engage with such products.

Multi-leg combos have crushed it in the sports betting world: accounting for >70% of all sportsbook revenues.

Having motivated why we think parlays are the right form factor when it comes to increasing the overall convexity and thus interest in prediction markets, we’ll try to build out a rough design for how parlays can be built on top of Event Futures.

First, let’s define a few important terms:

NB: Throughout this article we’ll refer to the “Lakers will win their 2025/26 NBA season opener” as LALW and “Nuggets will win their 2025/26 NBA season opener” market as NUGW for brevity.

The most trivial way to construct parlays would be to deploy another Event Future that represents the composite like “NUGW AND LALW”. However, this creates a few problems:

Design

Clearly, we believe making composite event futures out of singles is a bad idea. So what’s the solution? We propose a framework for JIT parlay creation where users submit trade intents through the UI and liquidity providers compete to fill them. The markets are not deployed onchain but the liquidity providers can use the underlying singles to hedge their position.

The entire lifecycle of the trade would look something like:

alt_text

NB: This design is exactly the same as intent-driven bridges or RFQs but is applied to a slightly different shape of market. Normally, RFQs are applied to aggregate liquidity from multiple sources to provide better pricing for 1 asset. Here we apply the RFQ style system to compose over multiple markets.

However, the liquidity provider is now exposed to the short leg of these markets. This is where the underlying singles come in, the liquidity provider can use singles to construct the right trade to hedge out their risk. Although the risk hedge is rarely going to be perfect, they can price the parlay in such a way to make the expected payoff worth the opportunity cost for them.

Example

Below we’ll go through the entire trade lifecycle for the a user trading NUGW ∧ LALW including some of the math surrounding hedging for liquidity providers:

NUGW ∧ LALW

Singles Definition:

Assuming independence of the two games we get: p_parlay = p_nugw × p_lalw = 0.60 × 0.50 = 0.30

If the liquidity provider quotes 0.40 to the user since their fair for the market is also 0.30, they’re effectively able to charge a premium because they take on risk and provide the user convexity that would be hard for the user to replicate if they were simply trading the singles.

Quote + size: The market maker quotes p_parlay = 0.40. User spends $100 to mint: q = 100/0.40 = 250 shares, with max payout of $250 (2.5x return).

Collateral Escrowed (1× isolated, fully collateralized):

Hedging:
While the maker may use singles to price the market, they can also set up hedged positions to reduce their max loss in the case that the parlay plays out. For example, if the Lakers are playing before the Nuggets and end up winning then the maker can hedge out their risk by going long NUGW. Assuming NUGW stays at 0.6, if the maker chooses to long 250 shares of NUGW they stand to make (1 - 0.6)*250 = 100 reducing their loss in the case that the trader wins from $150 to $50.

This example is of course overly simplistic but just goes to show how the underlying single and the parlay can go hand in hand to enable liquidity providers to give users completely new trading experiences. However, there is one glaring issue with this design with respect to liquidity provider capital efficiency that we’ll talk about in the next section.

Cross Margin

Fully collateralized, 1× isolated parlays are simple and safe, but capital‑inefficient for makers who hedge with singles. Without cross‑margin, a maker must post the parlay’s worst‑case loss and separately fund hedges—even when those hedges probably shrink the portfolio’s worst‑case P&L. A cross‑margin vault fixes this by recognizing offsetting risk and extending secured credit up to the initial‑margin relief created by the hedge, subject to buffers. Here’s how it works:

Extending the NUGW ∧ LALW to allow for cross-margin we get:

alt_text

The cross-margining logic can be extended by an underlying graph structure that draws correlations between Event Futures, such as partitions, mutual exclusivity, and implications. A margin engine can utilize the graph to efficiently calculate maintenance margin requirements and enhance capital efficiency.

Such a protocol needs to be built on the HyperEVM and interact with the HyperCore through the CoreWriter, since it’s not feasible to update the Event Futures metadata and store the relations on the HyperCore. The proposed graph structure would also grow on the order of O(n^2) , where n the number of markets which may even be better computed offchain and accessed on-chain via coprocessors.

A Note from the Authors

HIP-4 opens a new design space for builders to create net new financial products. This article is meant to showcase that interest and potential. By providing a rough design for how a popular sports betting product can be replicated on top of event futures, we aim to demonstrate just one of the many new and interesting financial products in the home of all of finance.

If you’d be interested in jamming around this design space more, feel free to reach out to us via twitter DMs! (our twitters are linked at the top of this post)