Hybrid Collateral Model

The system accepts a diversified basket of assets, typically including:

• Volatile Assets (e.g., ETH, BTC) for yield and liquidity.
• Stable Assets (e.g., stablecoins, LSTs) for price stability.
• Real-World Assets (RWAs) (e.g., tokenized treasuries) for uncorrelated, yield-generating backing. This composition mitigates systemic risk by avoiding over-concentration in any single asset class.

Each collateral type is assigned a risk weight and a liquidation haircut based on its volatility and liquidity profile. For example:

• High-volatility assets (e.g., altcoins) have lower loan-to-value (LTV) ratios (e.g., 60%).
• Stable, liquid assets (e.g., USDC) have higher LTV ratios (e.g., 85%). This tiered system protects the protocol by ensuring the collateral value exceeds the debt value even during market stress.

Hybrid models employ automated mechanisms to maintain peg stability for their issued stablecoins or debt positions:

• Recollateralization Triggers: If the volatile collateral ratio falls below a threshold, the system may mint new stablecoin debt to purchase more stable assets.
• Debt Buybacks: Excess stable collateral can be sold to buy back and burn stablecoin debt, tightening supply. These are core to protocols like MakerDAO's DAI and Frax Finance.

By blending high-yield volatile assets with stable collateral, the model aims to maximize returns on locked capital while maintaining safety. Yield farming rewards from volatile assets can subsidize borrowing costs or accrue to the protocol's treasury. This creates a more sustainable economic model than systems relying solely on low-yield, over-collateralized stable assets.

Liquidation processes are structured to prioritize the most stable assets to preserve system solvency:

1. Primary Liquidation: Attempts to sell the borrower's volatile collateral first.
2. Secondary Backstop: If insufficient, the protocol's shared stablecoin reserve (e.g., PSM in MakerDAO) is used to cover the bad debt.
3. Ultimate Recourse: As a last resort, system surplus buffers or governance token auctions may be triggered.

The addition of new asset types and adjustments to risk parameters (LTV, stability fees, debt ceilings) are managed by decentralized governance. Token holders vote on risk assessments from mandated oracles and risk teams. This ensures the collateral mix adapts to market conditions while maintaining decentralized oversight.

These are the primary, native assets of a blockchain ecosystem, such as ETH or SOL. They provide deep liquidity and high capital efficiency but introduce price volatility risk. Their value is highly correlated with the broader crypto market, making them susceptible to cascading liquidations during downturns.

• Examples: ETH, SOL, AVAX
• Role: Primary growth and liquidity driver
• Risk: High volatility and correlation

Assets pegged to a fiat currency, like the US Dollar, provide price stability and low volatility. They act as a shock absorber during market stress, reducing the risk of undercollateralization. Centralized (e.g., USDC, USDT) and decentralized (e.g., DAI, FRAX) variants offer different trade-offs in terms of counterparty risk and censorship resistance.

• Examples: USDC, DAI, FRAX
• Role: Stability anchor, volatility hedge
• Risk: Centralization or peg failure

Derivative tokens representing staked assets (e.g., stETH, SOL). They allow users to earn staking rewards while using the token as collateral, enhancing capital efficiency. They carry additional smart contract and slashing risk from the underlying staking protocol, but generally exhibit lower volatility than the base asset.

• Examples: stETH, jitoSOL, sAVAX
• Role: Yield-generating collateral
• Risk: Protocol slashing, de-pegging

Tokenized representations of off-chain assets like treasury bills, real estate, or corporate debt. They provide uncorrelated returns and stability from traditional finance, diversifying the collateral base. Their integration introduces complexities around legal compliance, custody, and valuation, creating unique oracle and off-chain dependency risks.

• Examples: Tokenized T-Bills, private credit
• Role: Diversification, yield stability
• Risk: Legal/regulatory, off-chain settlement

Tokens representing a user's share in an Automated Market Maker (AMM) liquidity pool (e.g., Uniswap v3 LP NFTs). Using them as collateral unlocks locked capital. However, they are subject to impermanent loss and concentrated liquidity risks, making their valuation complex and highly dependent on the performance of the underlying pool assets.

A hybrid model is governed by a risk engine that assigns different Loan-to-Value (LTV) ratios and liquidation thresholds to each asset class based on its risk profile. Stablecoins receive the highest LTV (~90%), while volatile assets receive much lower LTVs (~70%). This system continuously rebalances the portfolio to maintain overall protocol solvency and manage systemic risk.

A key financial metric representing the ratio of the value of collateral to the value of debt issued. It is the primary measure of safety for a loan or minted stablecoin in a collateralized system.

• Calculation: CR = (Value of Collateral / Value of Debt) * 100%.
• Minimum CR: The threshold below which liquidation is triggered.
• Importance: A higher ratio indicates a larger safety buffer against market volatility. Hybrid models often use different ratios for different asset tiers.

Collateral assets that inherently generate a yield or return while being locked in a protocol. This increases capital efficiency for users. A sophisticated hybrid model may integrate such assets.

• Examples: Staked ETH (stETH), LP tokens, or tokenized real-world assets that pay dividends.
• Benefit: Offsets borrowing costs or generates additional income for the depositor.
• Complexity: Requires the protocol to manage the yield accrual and associated risks (e.g., slashing for staked assets).