Comparison of Mainstream DeFi Derivative Strategies: Real Asset Backing vs Synthetic Asset Trading

Source: Chaos Labs X Account

Author: Chaos Labs

Compiled by: Deep Tide TechFlow

Written by research analyst @0xGeeGee

In both traditional finance and the cryptocurrency space, the scale of the derivatives market far exceeds that of the spot market. For example, as of now, the daily spot trading volume of Bitcoin is about $4 billion, while its derivatives trading volume reaches $53.89 billion (data source: Cryptoquant.com).

Bitcoin: Trading Volume Ratio (Spot vs Derivatives) --- Source: CryptoQuant

This trend began to accelerate in early 2021 and has continued to this day. The derivatives market in traditional finance has long surpassed the spot market, and the derivatives market in centralized exchanges (CEX) for cryptocurrencies has followed suit. In the decentralized finance (DeFi) space, derivatives have yet to surpass the spot market of decentralized exchanges (DEX). For instance, in the past 24 hours, @Uniswap v3 facilitated $1.3 billion in spot trades, while @HyperliquidX processed about $1 billion in derivatives trades (data source: Coingecko Data ).

Nevertheless, the gap is narrowing, and it is clear that as the ecosystem matures, on-chain derivatives may eventually surpass the spot market like other mature markets. While market demand is shifting towards derivatives, this growth requires secure and efficient trading platforms and models to support it.

Derivatives Trading Volume --- Source: DefiLlama

Understanding the different models that support the derivatives market is crucial for building the infrastructure that facilitates this transition. In this article, I will discuss the hard liquidity support model and the synthetic model.

Hard Liquidity Support Model

In the hard liquidity support model, traders transact with real assets, tokens, or stablecoins stored in a liquidity pool. These assets are effectively lent to traders for opening margin positions. Examples of this approach include @GMXIO , @JupiterExchange , @GearboxProtocol PURE, and @Contango xyz .

Liquidity providers (LPs) earn trading fees by depositing hard assets and may also receive rewards as counterparties to traders. Therefore, LPs' earnings depend on the performance of the assets in the pool, the utilization rate of the pool, and the traders' profits and losses in a model without mechanisms to balance long and short trading volumes.

Advantages:

Lower bankruptcy risk: The risk of system bankruptcy is lower because trades are backed by real assets.

Composable DeFi: Hard support models like GMX and Jupiter allow for the re-collateralization of liquidity pool tokens: $GLP and $JLP tokens can be used as collateral or staked in other DeFi applications, enhancing capital efficiency.

Lower trading/market-making incentive requirements: Since LPs act as counterparties or market makers, the importance of direct incentives is reduced. While LPs typically receive rewards through token incentives in the early stages, in the long run, the returns for providing liquidity mainly come from trading fees, reducing the complexity of designing balanced trading incentive programs.

Deepening market liquidity: The hard support model promotes deeper market liquidity by requiring a liquidity basket backed by actual assets. Over the past few years, this has also made protocols like GMX one of the most efficient places to exchange spot assets, as liquidity is concentrated in pools that can serve both derivatives and spot markets.

From the screenshot of DefiLlama, we can see the number of protocols and pools, including GLP and JLP yields.

Within this category, different sub-models have emerged based on how liquidity is acquired and shared:

GMX v1 and Jupiter: These protocols use globally shared liquidity pools, where all assets are pooled together. This model ensures deep liquidity and enhances composability by allowing liquidity providers to use a single token across different DeFi protocols.

GMX v2 and Gearbox's PURE: Introduced isolated liquidity pools with a modular architecture, where each asset or market has its dedicated liquidity pool. This reduces the systemic risk of the protocol, allowing it to support longer-tail, higher-risk assets. The risks and returns of each asset are independent, preventing a single asset from affecting the liquidity of the entire protocol and creating different risk/return profiles.

In this "hard liquidity support" model, we can also see how Contango operates. Although it is not an independent model, Contango runs on existing lending protocols (like Aave) to provide a margin decentralized exchange experience. It utilizes real assets borrowed from lending pools and flash loan capabilities to create leveraged positions.

Synthetic Model

The hard liquidity support model ensures safety and composability by requiring real assets as collateral, while the synthetic model takes a different approach.

In the synthetic model, trades typically do not rely on real assets for support; instead, these systems depend on order book matching, liquidity vaults, and price oracles to create and manage positions.

The designs of synthetic models vary widely—some rely on peer-to-peer order book matching, with liquidity provided by active market makers, who can be professional or managed through algorithmic vaults, and liquidity can be globally shared or market-isolated; others use purely synthetic methods, with the protocol itself acting as the counterparty.

What is a liquidity vault?

In synthetic derivatives models, a liquidity vault is a centralized liquidity mechanism that provides the funding source needed for trades, whether directly supporting synthetic positions or acting as a market maker. Although the structure of liquidity vaults may vary slightly across different protocols, their primary purpose is to provide liquidity for trading.

These liquidity vaults are typically managed by professional market makers (like Bluefin stablecoin pools) or algorithms (like Hyperliquid, dYdX unlimited, Elixir pools). In some models, they are purely passive counterparty pools (like Gains Trade). Generally, these pools are open to the public, allowing the public to provide liquidity and earn rewards by participating in platform activities.

Liquidity vaults can be shared across listed markets, as seen in Hyperliquid, or partially isolated, as in @dYdX unlimited, @SynFuturesDeFi , and @bluefinapp , with these methods having similar risks and returns to those previously mentioned.

Some protocols, like Bluefin, adopt a hybrid model, combining a globally managed liquidity vault by market makers and isolated algorithmic pools.

In synthetic models, liquidity is typically provided by active users (peer-to-peer matching), liquidity vaults (as a backup), and market makers who quote buy and sell prices on the order book. As mentioned earlier, in some purely synthetic models, like @GainsNetwork_io , the liquidity vault itself acts as the counterparty for all trades, thus eliminating the need for direct order matching.

Advantages:

The trade-offs of synthetic models differ from those of hard liquidity support models, but they also bring a range of advantages:

Capital efficiency: Synthetic models are highly capital efficient because they do not require direct 1:1 backing of physical assets. As long as there is sufficient liquidity to cover potential outcomes of active trades, the system can operate with fewer assets.

Asset flexibility: These systems are more flexible in terms of trading assets since positions are synthetic. There is no need to provide direct liquidity for each asset, allowing for a more diverse range of trading pairs and enabling faster—sometimes even semi-permissioned—listing of new assets.

This is particularly evident in Hyperliquid's pre-release markets, where the assets traded have not yet truly existed.

Better price execution: Since trades are purely synthetic, it is possible to achieve better price execution, especially when market makers are active on the order book.

However, these models also have some significant drawbacks:

Dependence on oracles: Synthetic models are highly dependent on price oracles, making them more susceptible to related issues such as oracle manipulation or delays.

Lack of liquidity contribution: Unlike hard support models, synthetic trading does not contribute to the global spot liquidity of assets, as liquidity only exists within the order book of derivatives.

Although decentralized exchanges still account for a relatively small share of overall perpetual contract trading volume (about 2% of the market) compared to centralized exchanges, diverse models are laying the groundwork for future real growth. The combination of these models, along with ongoing improvements in capital efficiency and risk management, will be key for decentralized exchanges to expand their market share in derivatives.

Perpetual Contract Trading Volume Distribution ------ Source: GSR Annual Report

Contribution of Chaos Labs

Chaos Labs plays a crucial role in risk management for hard support liquidity and synthetic models, catering to the specific needs of our partner platforms such as @GMX_IO , @dYdX , @SynFuturesDeFi , @JupiterExchange , @OstiumLabs , and @Bluefinapp .

As a long-term provider of risk analysis, Chaos Labs helps protocols manage leverage limits, liquidation thresholds, collateral requirements, and overall platform health through real-time risk assessments and simulations.

Chaos Labs' latest product, Edge Network, introduces a decentralized oracle system that helps mitigate risks associated with oracles, ensuring that both synthetic and hard support models can benefit from real-time, accurate price data. Edge has already been adopted as the primary oracle by well-known platforms like Jupiter.

Chaos Labs also collaborates with partners to develop optimized liquidity incentive programs to ensure a smooth trading experience and attract more liquidity.

Finally, Chaos Labs provides public dashboards for monitoring risk parameters on platforms such as GMX, Jupiter, Bluefin, and dYdX.