Ethena, Pendle and Aave: The Operation and Risks of DeFi Arbitrage Chain

As the popularity of Ethena continues to rise, a complex arbitrage chain is operating at high speed: collateralizing (e/s)USDe to borrow stablecoins on lending platforms, purchasing YT/PT from a certain DEX to generate returns, and some positions further supply PT back to the lending platform to leverage, thus obtaining Ethena points and other external incentives. This operation has caused the collateral exposure of PT on the lending platform to rise sharply, pushing the utilization rate of mainstream stablecoins above 80%, making the entire system more sensitive to any fluctuations.

This article will analyze the operational mechanism of this capital chain, exit routes, and the risk control design of related platforms in depth. However, merely understanding the mechanism is not enough; true experts need to upgrade their analytical framework. We often use data analysis tools to review the past, but we often overlook how to gain insights into various possibilities for the future, and we should first define the risk boundaries before considering returns.

Arbitrage Chain Operation: From Profit to System Impact

The operation of this arbitrage path is as follows: deposit eUSDe or sUSDe( on the lending platform as collateral for eUSDe with native yield), borrow stablecoins, and then purchase YT/PT on a certain DEX. YT corresponds to future yields, while PT can usually be bought at a discount due to stripped yields and can earn the difference when held until maturity and redeemed at a 1:1 ratio. However, what is truly attractive are external incentives such as Ethena points.

The obtained PT can be used as collateral on the lending platform to form a circular loan: "Collateralize PT → Borrow stablecoins → Buy PT/YT → Re-collateralize". This is done to leverage and speculate on high-elasticity returns such as Ethena points based on relatively certain收益.

This operation has had a significant impact on the lending market:

• USDe-supported assets have gradually become mainstream collateral on lending platforms, with their share once rising to about 43.5%, directly boosting the utilization rates of major stablecoins USDT/USDC.

• The USDe borrowing scale is approximately 370 million USD, of which about 220 million (≈60%) is used for leveraged PT strategies, with utilization soaring from about 50% to around 80%.

• The supply of USDe on lending platforms is highly concentrated, with the top two entities accounting for over 61%. This concentration, combined with circular leverage, amplifies both the returns and the system's vulnerability.

The pattern is clear: the more enticing the yield, the more crowded the cycle, and the more sensitive the entire system becomes. Any slight fluctuation in price, interest rates, or liquidity can be ruthlessly amplified by this leverage chain.

Increasing Difficulty of Exit: Structural Limitations of DEX

There are mainly two ways to deleverage or close the aforementioned cyclical positions:

1. Market-based exit: Sell PT/YT before maturity, exchange for stablecoins to repay and unlock.

2. Hold until maturity exit: Hold PT until maturity, 1:1 redeem the underlying asset for repayment. This method is slower, but more secure during market fluctuations.

The difficulty in exiting mainly arises from two structural limitations of a certain DEX:

1. Fixed Term: PT cannot be directly redeemed before maturity and can only be sold in the secondary market. To quickly reduce leverage, one must face the dual challenges of market depth and price volatility in the secondary market.

2. The "implied yield range" of AMM: The AMM of this DEX operates most efficiently within the preset implied yield range. Once market sentiment changes and the yield pricing exceeds this range, the AMM may "fail," and trades can only be executed on a thinner order book, leading to a sharp increase in slippage and liquidation risks. To prevent risk spread, lending platforms have deployed PT risk oracles: when the PT price drops to a certain bottom price, the market is directly frozen. This can avoid bad debts, but it also means that it will be difficult to sell PT in the short term, forcing one to wait for the market to recover or hold until maturity.

Therefore, exiting during stable market conditions is usually not difficult, but when the market begins to reprice and liquidity becomes crowded, exiting becomes a major friction point that requires advance planning.

The "Brakes and Buffers" of Lending Platforms: Making Deleveraging Orderly and Controllable

In the face of such structural friction, the lending platform has built-in a "brake and buffer" mechanism:

• Freezing and Floor Price Mechanism: If the PT price reaches the oracle floor price and maintains, the relevant market can freeze until expiration; after expiration, PT naturally decomposes into the underlying asset, followed by safe settlement/unlocking, in order to avoid liquidity mismatch overflow caused by fixed-term structures.

• Internal Settlement: In extreme cases, the liquidation reward is set to 0, forming a buffer first and then disposing of collateral in segments: USDe will be sold in the secondary market after liquidity is restored, while PT will be held until maturity to avoid passive selling on a thin order book, thereby amplifying slippage.

• Whitelist Redemption: If the lending platform obtains the Ethena whitelist, it can bypass the secondary market and directly redeem the underlying stablecoin using USDe, reducing impact and enhancing recovery.

• The boundary of supporting tools: When the liquidity of USDe is temporarily tight, Debt Swap can convert USDe-denominated debt into USDT/USDC; however, it is constrained by E-mode configuration, and migration has thresholds and steps, requiring more sufficient margin.

Ethena's "Adaptive Base": Supporting Structure and Custody Isolation

Ethena absorbs shocks through the "automatic transmission" mechanism:

• When the funding rate decreases or turns negative, Ethena reduces its hedging exposure and increases support for stablecoins; in mid-May 2024, the proportion of stablecoins once reached about 76.3%, before falling back to around 50%, which is still high compared to previous years, allowing for proactive relief during negative funding periods.

• In extreme LST penalty scenarios, the estimated net impact on the overall support for USDe is approximately 0.304%; a reserve of 60 million USD is sufficient to absorb such shocks, which account for only about 27% of it, thus the substantive impact on the peg and redemption is manageable.

• Ethena's assets are settled over-the-counter and isolated through third-party custodians. This means that even if the exchange encounters operational or repayment issues, these assets used as collateral are independent in ownership and protected. Under this isolation framework, an efficient emergency process is achieved: in the event of an exchange interruption, the custodian can void open positions after missing a certain number of settlement rounds, releasing the collateral, which helps Ethena quickly transfer hedging positions to other exchanges, thus greatly reducing the risk exposure window.

When misalignment primarily comes from "implied yield re-pricing" rather than damage to USDe support, under the protection of oracle freezing and layered disposal, the risk of bad debts is controllable; what really needs to be focused on is the tail events where the support side is damaged.

Six Major Risk Signals

The following 6 signals are highly correlated with lending platforms, a certain DEX, and Ethena, and can serve as a dashboard for daily monitoring:

1. USDe borrowing and utilization rate: Continuously track the total borrowing amount of USDe, the proportion of leveraged PT strategies, and the utilization rate curve. The utilization rate remains consistently above approximately 80%, significantly increasing the system's sensitivity.

2. Lending platform exposure and second-order effects of stablecoins: Focus on the proportion of assets supporting USDe in total collateral and the transmission effect on the utilization rate of core stablecoins such as USDT/USDC.

3. Concentration and Rehypothecation: Monitor the deposit proportion of top addresses; when the concentration of top addresses ( exceeds 50-60% as the sum of the top two ), be cautious of the liquidity impact that their coordinated operations may trigger.

4. Proximity of the Implied Yield Range: Check whether the implied yield of the target PT/YT pool approaches the boundaries of the AMM preset range; proximity to or exceeding the range indicates a decrease in matching efficiency and an increase in exit friction.

5. PT Risk Oracle Status: Pay attention to the distance between the PT market price and the minimum price threshold of the lending platform risk oracle; approaching the threshold is a strong signal that the leverage chain needs to "decelerate in an orderly manner."

6. Ethena Support Status: Regularly check the reserve composition announced by Ethena. Changes in the proportion of stablecoins reflect its adaptation strategy to funding rates and system buffering capacity.

You can set trigger thresholds for each signal and plan response actions in advance (. For example: Utilization ≥ 80% → Reduce the loop multiplier ).

Risk and Liquidity Management: Four Major Boundaries

Solidify these signals into 4 clear "boundaries" and operate around the "risk limit → trigger threshold → disposal action" closed loop:

1. Loop multiplier boundary

   • Limit: Set the maximum cycle multiple and the minimum margin redundancy.
   • Trigger: Utilization Rate ≥ 80% / Stablecoin borrowing rate rising rapidly / Interval proximity increasing.
   • Action: Reduce multiplier, add margin, pause new cycles; switch to "Hold until expiration" if necessary.

2. Time constraint ( PT ) boundary

   • Limit: Set a scale limit for positions that rely on "selling before expiration".
   • Trigger: Implied yield exceeds range / Market depth drops sharply / Oracle floor price approaches.
   • Actions: Adjust the cash and margin ratio, modify exit priority; set a "no increase only decrease" freezing period if necessary.

3. Oracle State Boundary

   • Limit: The minimum price difference with the oracle's bottom price ( buffer ) and the shortest observation window.
   • Trigger: Price difference ≤ preset threshold / Freeze signal triggered.
   • Action: Gradually reduce positions, increase liquidation warnings, execute Debt Swap / leverage reduction SOP, and increase data polling frequency.

4. Tool Friction Boundaries

   • Limit: Available amount of tools/time window and maximum acceptable slippage and cost.
   • Trigger: Borrowing interest rate or waiting time exceeds threshold / Trading depth falls below lower limit.
   • Action: Reserve fund redundancy, switch to alternative channels, and pause strategy expansion.

Conclusion

The arbitrage between Ethena and a certain DEX has created a transmission chain from "yield magnet" to "system resilience" involving the lending platform, the DEX, and Ethena. The cycle on the funding side has increased sensitivity, the structural constraints on the market side have increased the difficulty of exit, while each protocol provides a buffer through risk control design.

In the field of DeFi, the advancement of analytical capabilities is reflected in how we view and use data. We are accustomed to using data analysis tools to review the "past", which helps identify system vulnerabilities such as high leverage and concentration. However, historical data can only present a "static snapshot" of risks and cannot predict how these static risks will evolve into dynamic system collapses during market storms.

To gain insight into potential tail risks and deduce their transmission paths, it is necessary to introduce forward-looking "stress tests"—this is precisely the role of simulation models. They allow us to parameterize all risk signals, place them in a digital sandbox, and repeatedly simulate various extreme scenarios:

• ETH price plummeted 30% while funding rates turned negative, how long can positions be maintained?
• How much slippage is required for a safe exit?
• What should the minimum security margin be?

The answers to these questions cannot be directly derived from historical data, but can be anticipated through simulation modeling, ultimately forming a reliable execution manual. If you want to practice, you can choose the industry-standard framework cadCAD based on Python, or try the new generation platform based on cutting-edge GABM technology, which offers powerful visualization and no programming capabilities.