Self-Repaying Loan

The core technical prerequisite for a self-repaying loan. The collateral asset must natively accrue value or be deposited into a yield-generating strategy. Common examples include:

• Liquid Staking Tokens (LSTs) like stETH or rETH that accrue staking rewards.
• Vault Shares from yield aggregators (e.g., Yearn, Convex).
• Rebasing Tokens or Reward-Bearing Tokens. The protocol's smart contracts are programmed to automatically direct this yield to the outstanding debt balance.

A defining characteristic of the Alchemix model. Because the loan is structured to be repaid by its own collateral's yield, there is no required minimum collateral ratio and thus no traditional liquidation risk from price volatility, assuming yield remains positive. This creates a fundamentally different risk profile:

• Primary Risk: Protocol insolvency or a sustained drop in yield generation below expectations.
• Contrasts with overcollateralized loans (e.g., MakerDAO) which require active management to avoid liquidation.

A closely related DeFi primitive where the stablecoin itself is a yield-bearing asset. Examples include Aave's GHO (with facilitator strategies) or MakerDAO's Savings Dai (sDAI). While not a "loan" per se, holding these tokens represents a claim on underlying yield-generating collateral. The self-repaying loan model can be viewed as inverting this: instead of earning yield on a stablecoin balance, the yield is automatically applied to extinguish a debt balance.

While innovative, self-repaying loans carry specific risks:

• Yield Risk: If the generated yield falls below the loan's interest rate, the loan will not fully self-repay.
• Protocol Risk: Smart contract bugs or economic exploits in the underlying yield strategies.
• Illiquidity Risk: Some vault strategies may have withdrawal locks or delays.
• Complexity Risk: The multi-layered dependency on yield strategies increases systemic complexity and points of failure.

The primary benefit is the automatic repayment of interest or principal. The yield generated by the staked collateral (e.g., from staking rewards or liquidity provider fees) is programmatically used to pay down the loan balance. This creates a hands-off financial instrument where the loan can theoretically amortize to zero without any active intervention from the borrower, provided the yield exceeds the borrowing costs.

This structure enables leveraged yield farming strategies. A user can deposit an asset like ETH as collateral, borrow a stablecoin against it, and then use that stablecoin to acquire more ETH to stake. The yield from the amplified staking position is used to repay the loan, allowing the user to maintain exposure to potential ETH appreciation while earning yield on a larger position than their initial capital would allow.

If the yield from the collateral consistently covers the borrowing costs, the loan's principal balance decreases over time. This improves the loan's health ratio (collateral value / debt value), as the debt shrinks while the collateral remains. In a perfectly balanced system, this can theoretically eliminate liquidation risk from price volatility, as the debt is being paid down autonomously.

It serves users who are long-term bullish on an asset but need liquidity. Instead of selling the asset, they can use it as collateral for a self-repaying loan. They access capital for other uses while their asset continues to appreciate and generate yield, which services the debt. This is a non-dilutive financing method for crypto-native entities or individuals.

The model is not risk-free. Success depends on several critical assumptions:

• Sustainable Yield: The collateral's yield must reliably exceed the loan's interest rate.
• Protocol Risk: Dependence on the security of the underlying yield-generating protocol and the loan platform.
• Volatility Spikes: Sharp drops in collateral value can still trigger liquidation before the self-repayment mechanism can compensate.
• Smart Contract Risk: Complexity increases attack surface.

The core mechanism depends on the collateral asset (e.g., ETH, wBTC) generating yield. A sharp decline in the asset's price can outpace the yield earned, leading to:

• Negative repayment rate: The loan balance decreases slower than the collateral value falls.
• Increased risk of liquidation: The Loan-to-Value (LTV) ratio can rise dangerously even as the nominal debt shrinks.
• Impermanent loss for LP positions: If collateral is in a liquidity pool, price divergence can erode the yield-generating base.

The loan's "self-repaying" feature is only as reliable as its underlying yield source. Key risks include:

• Protocol risk: The yield-generating protocol (e.g., a lending market or liquidity pool) could be exploited, suffer a smart contract bug, or change its reward parameters.
• Yield volatility: APYs are rarely stable. A significant drop in available yield can stall debt repayment.
• Tokenomics risk: If yield is paid in a project's native token, its value and liquidity are critical. A token crash renders the repayment mechanism ineffective.

Automation does not eliminate liquidation risk; it changes its dynamics. Users must monitor:

• Health Factor / Collateral Ratio: This must be maintained despite both debt decrease and collateral value fluctuations.
• Liquidation triggers: A rapid market downturn can still trigger liquidation before the yield mechanism can compensate.
• Liquidation penalties: These are often higher than in traditional finance, resulting in a greater loss of collateral.

These loans involve complex, interconnected smart contracts, multiplying attack surfaces.

• Integration risk: Failures in the yield source, oracle, or debt management contract can break the repayment loop.
• Upgrade risks: Protocols may upgrade, potentially introducing bugs or changing interaction patterns.
• Oracle risk: Accurate price feeds for both collateral and debt are essential for calculating health factors and triggering liquidations.

The long-term sustainability of the model is not guaranteed.

• Negative interest rate scenarios: If the cost of borrowing (interest rate) exceeds the yield generated by the collateral, the loan balance will increase.
• Design dependency: The entire model depends on persistent, positive yield opportunities in DeFi, which may not last.
• Gas cost inefficiency: For smaller loans, transaction (gas) fees for rebalancing or claiming yield can eat into the repayment benefits.

The automated financial engineering of self-repaying loans exists in a regulatory gray area.

• Tax treatment: The continuous, automated generation of yield and debt repayment may create complex taxable events.
• Security classification: Regulators may scrutinize whether the bundled product constitutes a security.
• Compliance risk: Future regulations targeting DeFi lending or automated financial products could impact availability or design.