Algorithmic stablecoins, explained

Today’s most popular stablecoins are fiat-backed stablecoins. These are relatively simple in their design: for every blockchain-based token issued, a dollar/euro/yen is held in reserve.

Any person holding these tokens should be able to redeem each token for one unit of the underpinning currency. The result is a token that trades at—or close to—the price of the underlying fiat currency. After all, no one wants to pay more than $1 for a token that can only be exchanged for a dollar, nor will anyone sell it at a discount.

Algorithmic stablecoins, in stark contrast, are not “backed” by any real-world asset(s). As the name indicates, their price is dictated entirely by algorithms. Let’s explore the ways these work.

A seigniorage (or dual-token) algorithmic stablecoin usually relies on two tokens: the stablecoin itself, and a second one called the bond token. Together, these two tokens work to maintain price stability via market incentives.

When the stablecoin trades above a dollar, it suggests that demand outpaces supply. The protocol rectifies this by minting new units and distributing them to ecosystem participants (e.g., those that hold a governance token). With this supply increase, it’s expected that prices will return to parity.

When the stablecoin trades below a dollar, supply exceeds demand, and the protocol must reduce the circulating supply. This is where the bond token comes into play. For example, if the stablecoin currently trades at $0.75, the protocol allows users to purchase the bond token at this price.

The bond token can be redeemed for $1 upon the stablecoin’s return to parity — meaning that buyers will make $0.25 on every bond they buy now. Meanwhile, the protocol reabsorbs the stablecoins used to buy bonds, effectively reducing the circulating supply, and returning the stablecoin to parity

Are there algorithmic stablecoins that operate with a single-token system? Yes, meet the rebasing stablecoin.

The principles of a rebasing stablecoin remain similar to those of the seigniorage variety: when prices exceed a dollar, the circulating supply is increased. When they drop below a dollar, the circulating supply decreases.

The key difference here is that rebase stablecoins have an elastic supply, with the number of tokens in circulation changing (rebasing) based on their price. To illustrate this, suppose that you have 10 tokens in your wallet, worth a total of $10. 

Should the token double in value (such that your 10 are now worth $20), the rebasing mechanism will halve the total supply of tokens. Even the ones in your wallet.

In this case, your holdings would be reduced from ten to five tokens (worth $10 at the current price). You haven’t lost any of your dollar value, but the supply has contracted to account for the value increase. Similarly, if your 10 tokens were to drop to a total value of $5, you’d expect your token holdings to double. 

While this may initially seem alarming, it ultimately makes no difference: you still retain your overall proportion of circulating tokens

Though not considered to be “pure” algorithmic stablecoins (in that they rely on collateralized stablecoins), fractional-algorithmic stablecoins are worth including in this segment.

As the name indicates, they use a fractional-algorithmic mechanism—a hybridized approach which partially collateralizes the system’s digital assets. 

With this approach, a portion of the stablecoin is backed by crypto assets — which can be either cryptocurrency (e.g., ETH) or another stablecoin (DAI, USDC, USDT, etc.). The remaining value is then made up of an algorithmic mechanism (usually involving supply adjustments, as seen above).

For reasons we’ll detail in the next section, current data suggests that the market prefers fiat-backed and, to a lesser extent, crypto-backed stablecoins, over their algorithmic counterparts. This is demonstrated by collateralized assets dominating the top stablecoins list by market capitalization.

That being said, algorithmic alternatives do have some advantages over traditional stablecoins.

Fiat-backed stablecoins are inherently trust-based: participants believe that a centralized party maintains 1:1 dollar reserves. This counterparty risk could prove catastrophic for their peg should it emerge that they don’t, or in case of regulatory interference.

Conversely, the algorithmic stablecoin model is based entirely on-chain — and it doesn’t require fiat backing. Anyone can audit the smart contracts that underpin the system.

Unencumbered by real-world reserves, algorithmic stablecoins can rapidly adjust their circulating supply to respond to increased demand, requiring no external intervention.

Due to their complex mechanisms, algorithmic stablecoins face some major disadvantages.

A de-pegging event occurs when a stablecoin loses its parity with the asset it tracks. This scenario is precisely what the mechanisms of the stablecoin aim to prevent — however, as historical examples have shown, they’re often insufficient at preventing the system’s collapse.

A de-peg can happen due to a fault in the code, or, more commonly, due to a loss of trust in the stablecoin. A notable example of this is the Terra collapse, which saw the UST stablecoin’s value crash, eliminating hundreds of billions in USD.

Lack of collateralization can be viewed both as a strength and weakness in a stablecoin system. While it eliminates centralized risks, it can also aggravate ‘death spirals’ in the case of a de-peg: without the safety net of underlying reserves, the slightest loss in confidence in the system could result in rapid sell-offs and price collapse.

In summary, among stablecoins, algorithmic ones are perhaps ‘truest’ to the crypto ethos — in theory, serving as a decentralized, stable store of value in an otherwise volatile cryptocurrency market.

Due to their complexity, however, attempts to establish one that stands the test of time have proved difficult. 

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