Algorithmic stablecoins have garnered significant attention following the Terra-Luna collapse, which resulted in the loss of over $40 billion in investor wealth within days in May 2022.
The unprecedented de-pegging of the TerraUSD stablecoin sent shockwaves through the financial sector, prompting lingering skepticism among crypto investors regarding algorithmic stablecoins. In this guide, we'll explore the fundamentals of algorithmic stablecoins and the associated risks.
Understanding Algorithmic Stablecoins
Algorithmic stablecoins represent a form of digital currency that utilizes computer algorithms and smart contracts to stabilize their value, typically pegging it to another asset like the U.S. dollar. Unlike centralized stablecoins such as Tether, backed by physical assets, or decentralized stablecoins like MakerDAO's DAI, over-collateralized with cryptocurrency, algorithmic stablecoins often operate under-collateralized, meaning they do not rely on a reserve of assets for their value.
These stablecoins employ a system of 'balancer' or 'share' tokens to absorb market volatility and maintain their peg. For instance, in the Terra blockchain system, the algorithmic stablecoin TerraUSD interacts with the governance token Luna to stabilize its value. When TerraUSD's value exceeds $1, Luna holders can profit by exchanging Luna for TerraUSD, and vice versa, to reduce supply and increase price.
However, the stability of algorithmic stablecoins hinges heavily on market demand. A decline in demand below a certain threshold can jeopardize the entire system. Additionally, these stablecoins rely on independent investors for price-stabilizing arbitrage, introducing significant risk. Despite potential downsides, the transparency and decentralization offered by algorithmic stablecoins may appeal to some users due to their reliance on auditable code and lack of regulatory oversight.
Types of Algorithmic Stablecoins
Algorithmic stablecoins can be categorized into various types, each with unique characteristics:
- Re-basing Algorithmic Stablecoins: These stablecoins employ price-elastic ERC-20 tokens, with a variable total supply adjusted regularly based on the token's price over time. Examples include the Ampleforth protocol.
- Seigniorage Algorithmic Stablecoins: This model comprises stablecoins and seigniorage shares, adjusting supply to maintain stable prices. Examples include Basis Cash and Luna/UST.
- Fractional Algorithmic Stablecoins: These stablecoins combine features of fully algorithmic and fully collateralized stablecoins to minimize over-collateralization and reduce custodial risks. Frax is a notable example, utilizing a partial-collateral protocol.
Trading Risks and Benefits
Algorithmic stablecoins entail a unique set of risks and benefits:
- Benefits: They embody decentralization, operating solely through auditable code without regulatory oversight. This eliminates user error risk and reintroduces seigniorage into the crypto ecosystem.
- Risks: However, their architecture presents inherent weaknesses. Vulnerable to de-pegging risk, these uncollateralized assets rely on market demand, and a decline in demand can lead to system collapse. Stability can be severely impacted during crises, resulting in significant price drops, as seen with TerraUSD.
Regulatory Landscape
Algorithmic stablecoins currently operate in a largely unregulated space, offering both opportunities and risks. While the absence of regulatory oversight fosters innovation and flexibility, it also exposes investors to fewer protections. Instances like the TerraUSD de-pegging event have underscored the need for greater regulation in this domain.
