Author: TEDAO
Introduction:
As the popularity of Ethena rises, a crowded arbitrage chain is operating at high speed: collateralizing (e/s) USDe to borrow stablecoins on Aave, purchasing Pendle's YT/PT for yield, and recycling some positions back to Aave to leverage, in order to earn Ethena points and other external incentives. The results are evident: the collateral exposure of PT on Aave has surged dramatically, pushing the utilization rate of mainstream stablecoins above 80%, making the entire system more sensitive to any fluctuations.
This article will delve into the operation of this capital chain, the exit mechanisms, and the risk control designs of Aave and Ethena. However, understanding the mechanisms is just the first step; true mastery lies in analyzing the upgrades of the framework. We often rely on data analysis tools (like Dune) to review the "past," but what is missing is how to foresee the various possibilities of the "future" and truly achieve — first delineate the risk boundaries, then discuss the returns.
How Arbitrage Works: From "Yield Side" to "System Side"
Let's first look at this arbitrage path: deposit eUSDe or sUSDe (sUSDe is the staked eUSDe, yielding native returns) in Aave, borrow stablecoins, then buy YT/PT in Pendle. YT corresponds to future yields, while PT can always be bought at a discount because it has stripped away the yield, held until maturity to redeem at a 1:1 ratio, profiting from the price difference. Of course, the real "big gain" comes from external incentives like Ethena points.
The PT obtained this way, since it can be used as collateral in Aave, becomes the perfect starting point for circular lending: "Collateralize PT → Borrow stablecoins → Buy PT/YT → Re-collateralize." This is done to leverage relatively certain returns to chase high-elasticity returns like Ethena points.
How has this capital chain rewritten the lending market?
Aave's Exposure and Second-Order Effects: Assets supported by USDe have gradually become mainstream collateral on Aave, with its share rising to about 43.5%, directly boosting the utilization rates of core stablecoins like USDT/USDC.
Crowding on the Borrowing Side: After introducing USDe eMode for PT collateralization, the borrowing scale of USDe reached approximately $370 million, of which about $220 million (≈60%) serves leveraged PT strategies, with utilization rates soaring from about 50% to around 80%.
Concentration and Re-collateralization: The supply of USDe on Aave is highly concentrated, with the top two entities accounting for over 61%. This concentration, combined with circular leverage, amplifies returns while exacerbating the system's fragility.
The rule here is simple: the more enticing the returns, the more crowded the cycle, and the more sensitive the entire system becomes. Any slight fluctuation in price, interest rates, or liquidity will be ruthlessly amplified by this leverage chain.
Note: The core on-chain data referenced in this article is primarily based on a report released by Chaos Labs on July 17, 2025, and related market observations. Due to the dynamic nature of on-chain data, readers are advised to check relevant data analysis platforms for the latest information.
Why "Exiting" Becomes Difficult: Pendle's Structural Constraints
So, how to exit? When reducing leverage or closing positions in the aforementioned circular positions, there are mainly two paths:
Market Exit: Sell PT/YT before maturity to exchange for stablecoins to repay and release collateral.
Hold Until Maturity Exit: Hold PT until maturity, redeeming the underlying asset at a 1:1 ratio to repay. This path is slower but more stable during market fluctuations.
Why does exiting become difficult? The challenges mainly stem from two structural constraints of Pendle:
Fixed Term: PT cannot be directly redeemed before maturity and can only be sold in the secondary market. To "quickly reduce leverage," one must watch the secondary market, enduring both depth and price volatility.
AMM's "Implied Yield Range": Pendle's AMM operates most efficiently within a preset implied yield range. Once market sentiment changes and yield pricing exceeds this range, the AMM may "deactivate," forcing trades to occur on thinner order books, sharply increasing slippage and liquidation risks. To prevent risk spillover, protocols like Aave deploy PT risk oracles: when the PT price drops to a certain floor price, the market is frozen. This can prevent bad debts but also means that you may find it difficult to sell PT in the short term, having to wait for the market to recover or hold until maturity.
Thus, exiting is usually not difficult when the market is stable, but when the market begins to reprice and liquidity becomes crowded, exiting becomes a major friction point that requires advance preparation.
Aave's "Brakes and Buffers": Making De-leveraging Orderly and Controllable
In the face of such structural friction, how do lending protocols (like Aave) control risk? They have built-in a set of "brakes and buffers" mechanisms:
Freezing and Floor Price Mechanism: If the PT price touches and maintains the oracle's floor price, the relevant market can be frozen until maturity; after maturity, PT naturally decomposes into the underlying assets, allowing for safe liquidation/release, minimizing liquidity misalignment spillover caused by fixed-term structures.
Internal Liquidation: In extreme cases, the liquidation reward is set to 0, forming a buffer before segmenting the disposal of collateral: USDe is sold in the secondary market after liquidity recovery, while PT is held until maturity to avoid passive selling on a thin order book in the secondary market, thus amplifying slippage.
Whitelist Redemption: If the lending protocol obtains an Ethena whitelist, it can bypass the secondary market and directly redeem the underlying stablecoins with USDe, reducing impact and enhancing recovery.
Boundary of Supporting Tools: When USDe liquidity is temporarily tight, Debt Swap can convert USDe-denominated debt into USDT/USDC; however, due to E-mode configuration constraints, migration has thresholds and steps, requiring more sufficient collateral.
Ethena's "Adaptive Base": Supporting Structure and Custody Isolation
While lending protocols have "brakes," the asset support side requires Ethena's "automatic transmission" to absorb shocks.
On the supporting structure and funding rate status: When funding rates decline or turn negative, Ethena reduces hedge exposure and increases stablecoin support; in mid-May 2024, the stablecoin proportion once reached ~76.3%, then fell back to ~50%, still high compared to previous years, allowing for proactive pressure relief during negative funding rate periods.
Further, from the perspective of buffer capacity: In extreme LST confiscation scenarios, the net impact on the overall support of USDe is estimated at about 0.304%; a reserve of $60 million is sufficient to absorb such shocks (only about 27% of it), thus the substantive impact on anchoring and repayment is controllable.
Custody and isolation of assets are key: Ethena's assets are not directly stored in exchanges but are settled off-exchange and isolated through third-party custodians (like Copper, Ceffu). This means that even if the exchange itself encounters operational or repayment issues, these collateralized assets are independent and protected in ownership. Under this isolation framework, efficient emergency processes can be implemented: if an exchange is interrupted, custodians can nullify unclosed positions after missing a certain number of settlement rounds, releasing collateral and helping Ethena quickly migrate hedge positions to other exchanges, significantly shortening the risk exposure window.
When misalignment mainly comes from "implied yield repricing" rather than damage to USDe support, under the protection of oracle freezing and layered disposal, bad debt risk is controllable; the real focus should be on preventing tail events that damage the support side.
What You Should Pay Attention To: 6 Risk Signals
Having discussed the theory, what specific indicators should we look at? The following six signals are highly correlated with the interaction of Aave × Pendle × Ethena and can serve as a daily dashboard for monitoring.
USDe Borrowing and Utilization Rate: Continuously track the total borrowing amount of USDe, the proportion of leveraged PT strategies, and the utilization rate curve. Utilization rates consistently above ~80% significantly increase system sensitivity (the reporting period rose from ~50% to ~80%).**
Aave Exposure and Stablecoin Second-Order Effects: Monitor the proportion of USDe-supported assets in Aave's total collateral (e.g., ~43.5%) and the transmission effect on the utilization rates of core stablecoins like USDT/USDC.
Concentration and Re-collateralization: Monitor the deposit proportion of top addresses; when the concentration of top addresses (e.g., the top two combined) exceeds 50-60%, be wary of potential liquidity shocks caused by their coordinated actions (the reporting period peak was >61%).
Proximity to Implied Yield Range: Check whether the implied yield of the target PT/YT pool is approaching the boundary of the AMM preset range; proximity or exceeding the range indicates decreased matching efficiency and increased exit friction.
PT Risk Oracle Status: Pay attention to the distance between the market price of PT and the minimum price threshold of Aave's risk oracle; nearing the threshold is a strong signal that the leverage chain needs to "orderly decelerate."
Ethena Support Status: Regularly check the reserve composition published by Ethena. Changes in the stablecoin proportion (e.g., falling from ~76.3% to ~50%) reflect its adaptive strategy to funding rates and system buffer capacity.
Furthermore, you can set trigger thresholds for each signal and plan response actions in advance (e.g., utilization rate ≥80% → reduce circular multiples).
From Observation to Boundaries: Risk and Liquidity Management
These signals ultimately serve risk control. We can solidify them into four clear "boundaries" and operate around the closed loop of "risk limits → trigger thresholds → disposal actions."
Boundary 1: Circular Multiples
While circular leverage enhances returns (especially when combined with external incentives), it also amplifies sensitivity to price, interest rates, and liquidity; the higher the multiple, the smaller the exit margin.
Limit: Set a maximum circular multiple and minimum collateral redundancy (e.g., LTV/Health Factor lower limit).
Trigger: Utilization rate ≥ 80% / Stablecoin borrowing rates rising rapidly / Proximity to range increasing.
Action: Reduce multiples, supplement collateral, pause new cycles; if necessary, switch to "hold until maturity."
Boundary 2: Term Constraints (PT)
PT cannot be redeemed before maturity; "hold until maturity" should be viewed as a regular path rather than a temporary measure.
Limit: Set a scale limit for positions relying on "selling before maturity."
Trigger: Implied yield exceeds the range / Market depth drops sharply / Oracle floor price approaches.
Action: Increase cash and collateral ratios, adjust exit priorities; if necessary, set a "only reduce, not increase" freeze period.
Boundary 3: Oracle Status
When prices approach the minimum price threshold or trigger freezing, it indicates that the link is entering an orderly deceleration phase for de-leveraging.
Limit: The minimum price difference (buffer) from the oracle floor price and the shortest observation window.
Trigger: Price difference ≤ preset threshold / Freezing signal triggered.
Action: Gradual position reduction, increase liquidation alerts, execute Debt Swap / de-leveraging SOP, and enhance data polling frequency.
Boundary 4: Tool Friction
Debt Swap, eMode migration, etc., are effective during tight periods, but there are frictions such as thresholds, waiting, additional collateral, and slippage.
Limit: Available tool limits/time windows and maximum tolerable slippage and costs.
Trigger: Borrowing rates or waiting times exceed thresholds / Trading depth falls below limits.
Action: Reserve capital redundancy, switch to alternative channels (gradual liquidation/hold until maturity/whitelist redemption), and pause strategy expansion.
Conclusion and Future Directions
In summary, the arbitrage between Ethena and Pendle has formed a transmission chain from "yield magnetism" to "system resilience" through Aave, Pendle, and Ethena. The circular funding has heightened sensitivity, while structural constraints on the market have raised exit thresholds, and the protocols provide buffers through their respective risk control designs.
In the DeFi space, the advancement of analytical capabilities is reflected in how we view and use data. We are accustomed to using data analysis tools like Dune or DeFiLlama to review the "past," such as tracking changes in positions of top addresses or trends in protocol utilization rates. This is important as it helps us identify system vulnerabilities like high leverage and concentration. However, its limitations are also evident: historical data presents a "static snapshot" of risks but cannot tell us how these static risks will evolve into dynamic system collapses when market storms hit.
To uncover these hidden 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 the risk signals mentioned in this article (utilization rates, concentration, prices, etc.) and place them into a digital sandbox (a joint model composed of the core mechanisms of Aave, Pendle, and Ethena), repeatedly questioning "What if…?":
If ETH price drops by 30% while funding rates turn negative, how long can my position hold?
How much slippage do I need to endure for a safe exit?
What should the minimum safe collateral be?
The answers to these questions cannot be directly found from historical data but can be anticipated through simulation modeling, ultimately helping you form a truly reliable execution manual. For practical implementation, you can choose the industry-standard framework cadCAD based on Python, or try the next-generation platform HoloBit, which is based on cutting-edge Generative Agent-Based Modeling (GABM) technology, providing powerful visualization and no-code functionality.
免责声明：本文章仅代表作者个人观点，不代表本平台的立场和观点。本文章仅供信息分享，不构成对任何人的任何投资建议。用户与作者之间的任何争议，与本平台无关。如网页中刊载的文章或图片涉及侵权，请提供相关的权利证明和身份证明发送邮件到support@aicoin.com，本平台相关工作人员将会进行核查。
