What are Algorithmic Stablecoins?

Looking to earn interest on stablecoins? Try PennyWorks for low-risk stablecoin lending!

Stablecoins are digital currencies whose value is tied to that of another asset — most often the U.S. dollar — that are designed to reduce the inherent volatility of cryptocurrencies. Stablecoins are primarily used to facilitate efficient and simple cryptocurrency trading although they are increasingly used for payments within decentralized applications (dApps), remittances and settlement, among other use cases.

Three types of stablecoins currently exist — fiat-collateralized stablecoins, crypto-collateralized stablecoins and algorithmic stablecoins. While the definitions of each type of stablecoin is still blurred, each type has advantages and disadvantages. Currently, fiat-collateralized stablecoins have the strongest track record of price stability, and they have taken the lead in terms of corporate and institutional usage. 

However, while fiat-collateralized stablecoins are by far the most prevalent, algorithmic stablecoins are rapidly gaining popularity. New sets of algorithmic stablecoins are employing innovative design mechanisms and offering additional benefits beyond simple dollars on the blockchain. The following discussion explores what algorithmic stablecoins are and how they work. 

How do Algorithmic Stablecoins Work?

Algorithmic stablecoins are digital currencies that are designed to achieve price stability. They use algorithms to incentivize market participant behavior and to manipulate circulating supply so that any given coin price should –– in theory –– stabilize around the peg. 

Algorithmic stablecoins are the epitome of true decentralization, without regulatory bodies to maintain or watch over the proceedings. The code is what is responsible for both the supply as well as the demand, alongside the target price. In theory, algorithmic stablecoins are capable of achieving price stability in the absence of tangible collateral — eliminating the possibility of errors for the user’s end. These foundational principles offer a scalable solution that is not offered by any other set on the market as of now. 

Algorithmic stablecoins fall into 3 main categories.

Rebasing Stablecoins

Rebasing algorithmic stablecoins manipulate the base supply to maintain the peg. Effectively, the protocol burns (removes) supply and mints (adds) supply from circulation in proportion to the coin’s deviation from the $1 peg.

If the stablecoin is greater than $1, the protocol mints coins. Alternatively, if the stablecoin’s price is less than $1, the protocol burns coins. Overall, this process ensures that the actual value of how much money you have doesn't change; instead, the protocol only changes how much each coin is worth individually — increasing or decreasing the circulating supply according to specific supply and demand fluctuations.

The expansion and contraction of the circulating token supply is referred to as a rebase mechanism. 

Ampleforth (AMPL)

Ampleforth (AMPL) is an example of a rebase algorithmic stablecoin, with a rebase scheduled every 24 hours and a target price of $1. If the price of AMPL dips below $1, the current supply contracts during rebase, increasing the value of each token. Conversely, if the price of AMPL exceeds $1, the circulating supply expands during rebase, reducing the value of each AMPL token. 

From a user’s point of view, the amount of tokens in each wallet will decrease or increase accordingly. However, the total value of each wallet does not change, thanks to the rebasing mechanism. Other rebase tokens include RMPL, YAM and BASED.

Seigniorage Stablecoins

Seigniorage stablecoins attempt to use one or two additional tokens to absorb the volatility of the stablecoin. Through this process, one coin is minted or burned to control the vale of another. 

Take a three-token system consisting of a stablecoin, share and bond token. When the stablecoin is above the target peg, the protocol will mint more stable coins and distribute them to the share token holders, who then may sell them, increasing the supply and decreasing the price.

On the other hand, when the stablecoin’s price is below the target, the protocol will sell bond tokens at a discount for the stablecoin, reducing the stablecoin’s supply and raising the price back up. Users who purchased the bonds at a discount then make money as the stablecoin rises in value. 

In theory, seigniorage-based stablecoins may work; however, in reality, these types of stablecoins suffer from runs on the bank as the bonding incentives generally are not strong enough to entice users to step in during a price spiral, especially without a collateral backstop.

The Terra Luna/UST Relationship

Terra Lab's Luna and UST cryptos were an example of a seigniorage stablecoin. Unlike fiat-backed stablecoins such as USDC and BUSD, UST is not backed by real assets. Instead, the value of UST is backed by a sister token called LUNA and relies on algorithms to maintain its $1 peg. 

When UST's price rises too high, its algorithms mint more LUNA to lower its price — or the reverse if the price falls too low. LUNA is meant to serve as a kind of shock absorber for UST's price. For this stabilizing price mechanism to work, users can redeem 1 UST for $1 worth of LUNA, even if UST is worth less than $1.

However, despite the dynamic nature of the protocol, the Terra ecosystem recently collapsed. Before the collapse, over $2 billion worth of UST was unstaked (taken out of Anchor Protocol), and hundreds of millions of that was immediately sold, ultimately pushing the price of UST down to 91 cents. 

In theory, UST’s sister token LUNA should have been able to offset the decrease in demand for UST; however, bearish sentiment in the broader cryptocurrency market resulted in LUNA's price, like many other altcoins, crashing over the last few weeks. 

This situation caused a huge problem because it meant that the amount of LUNA that needed to be sold in return for UST drastically increased. With UST depegged and an increasing amount of LUNA dumped on the open market, fear that there wouldn't be enough funds in the Terra project to properly back the value of UST magnified. This further increased the selling pressure on LUNA and led to the collapse of UST — the value of LUNA token dropped by 99.99% and the value of UST plummeted to around $0.03. For further explanation of the UST situation click here.

Fractional-Algorithmic Stablecoins

Fractional stablecoins refer to stablecoins that are partially backed by collateral and partially stabilized algorithmically.  These stablecoins have a collateralization ratio of less than or equal to 100%, meaning that the stablecoin can be backed by fewer real dollars or cryptocurrency –– a fraction thereof –– than its supposed total worth.

This design improves capital efficiency as fewer dollars are required to remain idle as collateral. In other words, fractional stablecoins have a larger circulating value than they do collateral. Similar to other types of algorithmic stablecoins, fractional stablecoins use algorithmic mechanisms to prevent bank runs through game theory and economic incentives. 

If the stablecoin is overcollateralized, then the algorithmic systems burn available stablecoins to reduce total supply thereby pushing the price back to $1. If the price rises above $1, the algorithmic system produces new stablecoins until the price returns to $1. 

Purely algorithmic stable coins such as rebase stablecoins often experience extreme periods of volatility that contradicts the sole purpose of being a stablecoin. A fractional stablecoin attempts to solve this problem by reducing the pressure on algorithms to support the peg.  

Frax Coin

Frax is a stablecoin protocol backed by both mathematical algorithms and asset collateralization. The project aims for its token to function as trustless, scalable and stable on-chain money. The Frax crypto protocol uses two different asset classes: the Frax (FRAX) stablecoin (which is designed to maintain a peg to $1 USD) and the Frax Shares (FXS) utility and governance token (which is used to support the platform’s various functions).

The Frax minting and redeeming mechanism is pivotal to maintaining FRAX token’s stable peg to USDC. Essentially, FRAX can be minted by any user who supplies not only collateral tokens (currently USDC) but also governance tokens (FXS). The percentage proportion of each token is given by the Frax collateral ratio which changes based on supply and demand. For example, a 50% collateral ratio means that one dollar of FRAX can be minted by providing $0.50 USDC and $0.50 worth of FXS. This mechanism works in reverse when a user chooses to redeem their Frax for the USDC and FXS initially provided. 

When new FRAX stablecoins are minted, FXS is burned proportionally to the uncollateralized amount. Using the previous example’s collateralization ratio of 50%, for every FRAX token minted, $0.50 of FXS is burnt. This means that by minting more FRAX, the circulating quantity of FXS is reduced.

Nonetheless, as usage and adoption increases, more FRAX tokens are supposed to be minted than redeemed, resulting in substantial increases of FXS being burned. As the circulating supply of FRAX increases sufficiently, the discrepancy between demand and supply for FXS is meant to act as a shock absorber for price stability of FRAX. 

Why are Algorithmic Stablecoins Risky?

One of the most important factors in an explanation of algorithmic stablecoins are the risks associated with them. Here are some of the notable risks you can find with algorithmic stablecoins:

1. Algorithmic stablecoins are prone to devaluation risk and speculative attacks when they are under collateralized. 
2. The crash in the value of one coin in an algorithmic pair can have a knock-on effect on the other. This happened in the LUNA/UST catastrophe, whereby the liquidation of Terra-native digital assets was far quicker than the algorithmic stabilizer could take effect.

Are Algorithmic Stablecoins Legit?

The idea of algorithmic stablecoins certainly seems to hold merit in terms of ensuring decentralization without regulatory bodies. The lack of requirements for tangible assets mitigates the chance of human error and also improves capital efficiency and scalability. 

Despite this, many complex factors can too easily influence an algorithmic stablecoin’s value and cause significant financial loss. Nonetheless, as new examples of algorithm-backed stablecoins emerge, it is important to do your research and understand what you are investing your money in.