In terms of support structure and funding rate status: When funding rates fall or turn negative, Ethena reduces hedging exposure and increases stablecoin support; in mid-May 2024, the proportion of stablecoins reached ~76.3% at one point, then fell back to ~50%, still higher than in previous years, and can proactively reduce pressure during negative funding cycles. Furthermore, looking at the buffer capacity: In the extreme LST slashing scenario, the net impact on USDe's overall backing is estimated to be approximately 0.304%. The $60 million reserve is sufficient to absorb such a shock (accounting for only approximately 27%), so the actual impact on the peg and repayment is manageable. Asset custody and segregation are key: Ethena's assets are not held directly on the exchange, but rather through over-the-counter settlement and asset segregation with third-party custodians (such as Copper and Ceffu). This means that even if the exchange itself experiences operational or repayment issues, the ownership of the collateral assets remains independent and protected. This isolated architecture enables efficient emergency response procedures. If an exchange experiences an outage, the custodian can void open positions after missing a certain number of settlement rounds, releasing collateral and helping Ethena quickly migrate hedged positions to other exchanges, significantly shortening the risk exposure window. When the dislocation primarily stems from "implied yield repricing" rather than a loss of USDE backing, the bad debt risk can be managed with the protection of oracle freezes and tiered disposals. The real focus is on tail events involving backing losses. What You Should Watch For: Six Risk Signals Now that we've covered the theory, what specific indicators should we look for? The following six signals are highly correlated with the correlation between Aave, Pendle, and Ethena and can be used as daily dashboards for monitoring. USDe Borrowing and Utilization: We continuously track USDe's total borrowing volume, the proportion of leveraged PT strategies, and the utilization curve. Utilization has consistently remained above ~80%, and system sensitivity has significantly increased (from ~50% to ~80% during the reporting period). Aave Exposure and Second-Order Effects of Stablecoins: Pay attention to the proportion of USDe-backed assets in Aave's total collateral (e.g., ~43.5%) and its transmission effect on the utilization of core stablecoins such as USDT/USDC. Concentration and Rehypothecation: Monitor the deposit ratio 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 coinciding operations (peak during the reporting period >61%). Closeness to the Implied Yield Range: Check whether the implied yield of the target PT/YT pool is close to the boundaries of the AMM's preset range; closeness or exceeding the range means reduced matching efficiency and increased exit friction. PT Risk Oracle Status: Monitor the distance between the PT market price and the Aave Risk Oracle's lowest price threshold; approaching the threshold is a strong signal that the leverage chain needs an orderly deceleration. Ethena Support Status: Regularly review Ethena's published reserve composition. Changes in the proportion of stablecoins (e.g., from ~76.3% to ~50%) reflect its adaptation strategy to funding rates and the system's buffer capacity. Furthermore, you can set trigger thresholds for each signal and plan responses in advance (e.g., utilization rate ≥ 80% → reduce the recycle factor). From Observation to Boundaries: Risk and Liquidity Management Ultimately, these signals serve risk control. We can solidify them into four clear "boundaries" and operate within a closed loop of "risk limit → trigger threshold → resolution action." Boundary 1: Revolving Leverage While increasing returns (when combined with external incentives), revolving leverage also amplifies sensitivity to price, interest rates, and liquidity; the higher the leverage, the less room for exit. Limits: Set a maximum revolving leverage and minimum margin margin (such as a lower limit for LTV/Health Factor). Triggers: Utilization ≥ 80% / Rapidly rising stablecoin borrowing rates / Increased range convergence. Actions: Reduce leverage, replenish margin, suspend new rotations; switch to "hold to maturity" if necessary. Boundary 2: Term Constraint (PT) PTs cannot be redeemed before maturity; "hold to maturity" should be considered a regular approach rather than a temporary fix. Limits: Set a size cap on positions that rely on "sell before maturity." Triggers: Implied yield exceeds range / market depth plummets / oracle floor price approaches. Actions: Increase cash and margin ratios, adjust exit priorities; set a "reduction-only, no increase" freeze period if necessary. Boundary 3: Oracle Status: When the price approaches the lowest price threshold or triggers a freeze, the chain enters an orderly deceleration and deleveraging phase. Limits: The minimum price difference (buffer) and the shortest observation window with the oracle floor price. Trigger: Price difference ≤ preset threshold / freeze signal triggered. Actions: Phased position reduction, increased liquidation alerts, execution of debt swap/deleveraging SOPs, and increased data polling frequency. Boundary 4: Tool Friction: Debt swaps and eMode migrations are effective during periods of stress, but they carry friction such as thresholds, waiting times, additional margin, and slippage. Limits: Available quota/time window for the instrument and maximum tolerable slippage and cost. Triggers: Borrowing interest rate or waiting time exceeds threshold/trading depth falls below lower limit. Actions: Reserve capital redundancy, switch to alternative channels (gradually close positions/hold until maturity/redemption through whitelist), and suspend strategy expansion. Conclusion and Future Directions Overall, the Ethena x Pendle arbitrage connects Aave, Pendle, and Ethena into a transmission chain from "yield magnetism" to "system resilience." The circulation of funds increases sensitivity, structural constraints on the market raise exit barriers, and the protocols provide buffers through their respective risk control designs.
In the DeFi field, the advancement of analytical capabilities is reflected in how to view and use data. We are accustomed to using data analysis tools such as Dune or DeFiLlama to review the "past", such as tracking changes in the positions of top addresses or trends in protocol utilization. This is very important, as it can help us identify systemic vulnerabilities such as high leverage and concentration. But its limitations are also obvious: historical data shows a "static snapshot" of risks, but it cannot tell us how these static risks will evolve into dynamic system collapses when market storms come.
To clearly see these potential tail risks and deduce their transmission paths, it is necessary to introduce forward-looking "stress testing" - this is exactly the role of simulation models. It allows us to parameterize all the risk signals mentioned in this article (utilization, concentration, price, etc.) into a digital sandbox (a joint model of the core mechanisms of the Aave, Pendle, and Ethena protocols), repeatedly asking, “What if…?”:
If the price of ETH plummets 30% and the funding rate turns negative, how long can I hold my position?
How much slippage do I need to withstand to exit safely?
What is the minimum safety margin?
The answers to these questions cannot be found directly in historical data, but they can be predicted in advance through simulation modeling, ultimately helping you develop a truly reliable execution playbook. To get started, choose cadCAD, an industry-standard Python-based framework, or try HoloBit, a next-generation platform based on cutting-edge Generative Agent-Based Modeling (GABM) technology, which provides powerful visualization and code-free capabilities.
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