Algorithmic Stablecoin

A rebasing (or elastic supply) algorithm automatically adjusts the token supply held in all wallets to maintain the target price. When the price is above the peg, the protocol mints and distributes new tokens to holders, diluting the value per token. When below, it burns tokens from holders' wallets, increasing scarcity. This is a direct, proportional adjustment to user balances. Example: Ampleforth (AMPL).

This model uses a multi-token system, typically separating the stable asset from a governance or share token. When demand is high, the protocol mints new stablecoins and sells them for profit (seigniorage), distributing the proceeds to governance token holders or a treasury. When demand is low, the protocol sells governance tokens to buy back and burn the stablecoin, supporting its price. Example: The original Basis Cash model.

An AMO is a decentralized, permissionless module that autonomously executes monetary policy on-chain. Instead of a single rebasing or seigniorage action, AMOs can perform complex operations like providing liquidity, buying bonds, or managing collateral pools to influence supply and demand. This allows for more flexible and capital-efficient stability mechanisms. Primary Example: Frax Finance (FRAX).

This feature creates a two-sided market for stabilizing the peg. When the stablecoin trades below its target, users can buy discounted bonds (IOUs) with the stablecoin, which are redeemable for the full value later, effectively burning supply. When above peg, new stablecoins are minted and sold, with proceeds used to honor bond redemptions. This creates arbitrage incentives. Example: OlympusDAO's original (3,3) mechanism for OHM.

Many modern algorithmic stablecoins are not purely algorithmic; they are fractionally collateralized. A portion of the circulating supply is backed by on-chain assets (e.g., USDC, ETH), while the remainder is stabilized algorithmically. This hybrid model aims to reduce volatility and increase confidence by providing a collateral buffer. Example: Frax Finance started as a partially collateralized system.

A PSM is a smart contract that allows direct, 1:1 swaps between the algorithmic stablecoin and a deeply liquid, trusted collateral asset (like USDC). This creates a hard arbitrage floor at the peg price, as users can always redeem the stablecoin for its full value in collateral. It is a critical feature for maintaining strong pegs in hybrid systems. Example: MakerDAO's PSM for DAI, Frax Finance's PSM for FRAX.

Historical protocols reveal common failure modes for algorithmic designs:

• Reflexivity & Death Spirals: A falling native token price can break the mint/burn arbitrage, causing hyperinflation (e.g., Terra/LUNA).
• Oracle Reliance: Peg stability mechanisms are highly dependent on accurate, manipulation-resistant price oracles.
• Demand Assumptions: Most models fail without sustained, exogenous demand for the stablecoin itself, treating stability as an isolated system.
• Governance Attacks: Control over critical parameters (like collateral ratios) can be a centralization vector.

A death spiral occurs when a loss of confidence triggers a positive feedback loop of selling pressure, causing the stablecoin to depeg. This typically involves:

• The stablecoin's price falls below its peg (e.g., $0.95).
• The protocol's rebasing or seigniorage mechanism incentivizes users to burn the stablecoin for a discounted collateral asset.
• This increased sell pressure drives the price lower, breaking the peg permanently.
• Historical example: The collapse of Terra's UST, which depegged from its $1 target in May 2022.

Most algorithmic models depend on price oracles to determine the value of their assets and trigger stabilization mechanisms. These are critical attack vectors:

• Flash loan attacks can be used to manipulate oracle price feeds temporarily.
• A manipulated price can trigger incorrect minting or burning of stablecoins or governance tokens.
• This can drain protocol reserves or artificially inflate/deflate supply, leading to insolvency.
• Robust oracle design (e.g., time-weighted average prices, multiple sources) is essential for security.

While often decentralized in theory, algorithmic stablecoins face significant governance challenges:

• Proposal power is typically concentrated in holders of the protocol's volatile governance token.
• Malicious or poorly designed governance proposals can alter core parameters (e.g., collateral ratios, fees), destabilizing the system.
• Voter apathy can lead to low participation, making the protocol susceptible to takeover by a small, motivated group.
• This creates a single point of failure where governance decisions can compromise the entire system's stability.

The stability of an algorithmic stablecoin depends on rational actors responding predictably to economic incentives. Key flawed assumptions include:

• Reflexivity: The value of the governance token backing the system is often derived from faith in the stablecoin itself, creating a circular dependency.
• Inelastic Demand: Models assume sufficient demand for the stablecoin during a downturn to absorb selling pressure, which may not materialize in a crisis.
• Arbitrage Efficiency: They rely on arbitrageurs to correct price deviations instantly, but high gas costs or market illiquidity can hinder this mechanism.

Like all DeFi protocols, algorithmic stablecoins inherit blockchain and smart contract risks:

• Code vulnerabilities in the core stablecoin, oracle, or controller contracts can lead to exploits and fund loss.
• Composability risk: Integration across DeFi (e.g., as collateral in lending protocols) can create systemic risk. A depegging event can trigger cascading liquidations in interconnected protocols.
• Front-running: The public nature of blockchain transactions allows bots to front-run stabilization transactions (like burns or mints), extracting value and reducing system efficiency.

Algorithmic stablecoins operate in a complex and evolving regulatory landscape, posing non-technical risks:

• They may be classified as securities by regulators (e.g., the SEC), leading to enforcement actions against issuers.
• Lack of clear redemption rights for holders contrasts with collateralized models, creating legal ambiguity.
• Bank run scenarios and consumer losses increase regulatory scrutiny and the potential for restrictive legislation that could impact protocol operations or token viability.

A stablecoin backed by a reserve of assets, such as fiat currency, commodities, or other cryptocurrencies. This is the primary alternative model to algorithmic designs.

• Fiat-Collateralized: Backed 1:1 by bank deposits (e.g., USDC, USDT).
• Crypto-Collateralized: Overcollateralized with crypto assets to absorb volatility (e.g., DAI).
• Key Distinction: Provides direct asset backing, contrasting with the purely algorithmic, seigniorage-style model.

The core economic model for many algorithmic stablecoins, involving a two-token system to manage supply and demand.

• Stablecoin Token: The asset intended to maintain the peg (e.g., an "algorithmic dollar").
• Governance/Share Token: Absorbs volatility and captures system profits; its value is tied to demand for the stablecoin.
• Mechanism: When the stablecoin is above peg, new stablecoins are minted and sold for the share token. When below peg, share tokens are burned or new ones are sold to buy back and burn the stablecoin.

An alternative algorithmic method where the token supply is programmatically adjusted in all holders' wallets to affect the unit price.

• Supply Elasticity: The total supply expands or contracts based on market price.
• Wallet Balances: Each holder's balance changes proportionally, keeping their percentage of the total supply constant.
• Example: If the token is at $0.90, the protocol may reduce the total supply by 10%, causing each wallet's balance to decrease but aiming to raise the per-token price to $1.00.

A critical data feed that provides external price information (e.g., the USD price of the stablecoin) to the on-chain smart contracts.

• Essential Input: Algorithmic mechanisms cannot function without a trusted, accurate price feed.
• Security Risk: Oracle manipulation or failure is a major vulnerability, as incorrect price data can trigger faulty minting or burning logic.
• Decentralization: Systems use aggregated data from multiple sources (e.g., Chainlink) to improve reliability.

A catastrophic failure mode specific to algorithmic stablecoins, where a broken peg leads to a vicious cycle of devaluation.

• Trigger: Loss of confidence or market shock drives the price significantly below the peg.
• Mechanism: The protocol's incentive to burn share tokens or mint more stablecoins fails to restore demand, causing the stablecoin and share token to collapse in a reinforcing feedback loop.
• Historical Example: The collapse of Terra's UST in May 2022 is the canonical case study of a death spiral.

Key economic concepts explaining the inherent risks in algorithmic stablecoin design.

• Fragility: The system's stability is highly dependent on continuous growth and market confidence, making it vulnerable to black swan events.
• Reflexivity: The market price of the stablecoin directly influences the protocol's actions (minting/burning), which in turn influences market perception and price, creating a potentially unstable feedback loop.
• Design Challenge: Balancing these forces is the central engineering and economic problem for algorithmic models.