Fractional Reserve Algorithmic Model

The core algorithmic function in these models. The protocol automatically adjusts the required collateral ratio or reserve composition based on on-chain oracle data.

• Expansion: Lowering the CR during high demand to increase stablecoin supply.
• Contraction: Raising the CR during low demand or market stress to protect the reserve.
• Goal: This dynamic adjustment aims to maintain the peg while optimizing capital efficiency and protocol solvency without centralized intervention.

A critical property where the stability mechanism itself influences market behavior, creating a self-reinforcing or self-correcting loop. In fractional reserve algorithmic models, this often manifests through:

• Redemption Arbitrage: If the stablecoin trades below peg, arbitrageurs redeem it for more valuable collateral, burning supply and increasing price.
• Collateral Confidence: A well-managed, transparent reserve increases trust, reducing redemption pressure. Poor management triggers a bank run dynamic, testing the fractional reserve's sufficiency.

The primary risk is a death spiral triggered by a sharp drop in the value of the reserve collateral (e.g., ETH, BTC). If the market value of the collateral falls below the outstanding value of the stablecoin, the protocol becomes under-collateralized. This can trigger mass redemptions, forcing liquidations that further depress collateral prices and break the peg. The 2022 collapse of Terra's UST is a canonical example of this failure mode.

These models are inherently vulnerable to liquidity crises. Since reserves are fractional, they cannot satisfy simultaneous redemption requests from all users. A loss of confidence can trigger a bank run, where users race to redeem their tokens before reserves are depleted. This is exacerbated by asymmetric information, where users cannot perfectly verify the protocol's real-time solvency, leading to preemptive withdrawals.

Protocol safety depends on correctly tuned algorithmic parameters (e.g., collateral ratios, stability fee rates, liquidation thresholds). These are often set and adjusted by decentralized governance. Risks include:

• Governance attacks where an attacker acquires enough tokens to pass malicious proposals.
• Suboptimal parameter updates that unintentionally destabilize the system.
• Slow reaction time of governance compared to fast-moving market crises.

The system's solvency calculations depend entirely on price oracles for the collateral assets. This creates a critical attack vector:

• Oracle manipulation (e.g., flash loan attacks) can provide false high prices, allowing users to mint excessive stablecoins against overvalued collateral.
• Conversely, a manipulated low price can trigger unnecessary, destabilizing liquidations of healthy positions.
• Oracle latency or failure can freeze critical protocol functions.

Beyond the economic model, the implementation carries standard DeFi risks:

• Smart contract vulnerabilities in the core minting, redemption, or liquidation logic.
• Composability risk: The protocol's tokens are integrated across the DeFi ecosystem. A failure can cause contagion, liquidating positions in lending markets and draining liquidity pools that hold the asset, amplifying losses.
• Upgradeability risks associated with proxy contracts or admin keys.

Fractional reserve models, especially those offering yield, may attract regulatory scrutiny as potential unregistered securities or operations resembling banking activities without a charter. Key uncertainties include:

• Securities laws: How regulators (e.g., SEC, ESMA) classify the governance tokens and the yield-bearing stablecoins.
• Banking laws: Whether the minting/redeeming functions constitute deposit-taking.
• Enforcement actions against developers or governance token holders.

A cryptocurrency designed to maintain a stable value relative to a target asset (e.g., USD) primarily through on-chain algorithms and smart contracts, rather than direct 1:1 backing. Core mechanisms include:

• Rebasing: Adjusting token supply in user wallets.
• Seigniorage: Minting and burning tokens based on market demand.
• Peg Stability Module (PSM): Facilitating 1:1 swaps with a reserve asset. This model is often contrasted with collateralized stablecoins like USDC.

The ratio of the value of collateral backing an asset to the value of the asset issued. In a fractional reserve model, this is less than 100% (e.g., 80%). It's a critical risk parameter that measures:

• Protocol Solvency: Ability to cover redemptions.
• Capital Efficiency: How much value can be created per unit of collateral.
• Safety Buffer: Protection against collateral value volatility. Managed by governance or algorithmic controllers.

The profit earned by the protocol when the market price of its stablecoin is above the peg. In algorithmic models, this profit is often used to:

• Mint and sell new tokens on the open market to push the price down.
• Fund a treasury or reserve for future stability operations.
• Distribute rewards to governance token stakers. It is the primary expansionary mechanism when demand is high.

An algorithmic adjustment of the token supply held in every wallet to move the market price toward the target peg. For example, if the price is below peg, the protocol reduces the supply in all wallets proportionally, making each remaining token more scarce. Key characteristics:

• Dilution/Payout: Affects all holders equally.
• Passive Action: Requires no user interaction.
• Elastic Supply: Total supply is variable. Used by early algorithmic coins like Ampleforth.

Automated Market Makers (AMMs) like Uniswap or Curve are essential for providing a liquid market for the stablecoin. The depth and composition of these pools directly impact:

• Peg Defense: Large pools can absorb buy/sell pressure.
• Arbitrage Opportunities: Encourage traders to correct price deviations.
• Protocol-Owned Liquidity (POL): Reserves can be deployed as liquidity to reduce reliance on mercenary capital.

A stablecoin model where the value of the locked collateral exceeds the value of the minted stablecoins (e.g., 150% collateralization). This is the dominant model in decentralized finance (DeFi), used by MakerDAO's DAI. It contrasts with the fractional reserve model by prioritizing:

• Capital Inefficiency for greater security.
• Liquidation mechanisms to maintain solvency.
• Direct user collateralization, not a shared reserve.