Algorithmic Stablecoins: A Beginners Guide

Stablecoins act as a calm and composed substitute for the unpredictable cryptocurrency – they behave as the responsible ones by keeping their value pegged to a stable asset like US Dollar or gold. Stablecoins are a reliable alternative to volatile cryptocurrencies, offering traders a stable option to avoid the market fluctuations of crypto.

Originally, algorithmic stablecoins were anticipated to function like other stablecoins within the cryptocurrency ecosystem. However, they failed to fulfill their expected role as a dependable alternative to volatile cryptocurrencies. Rather Algorithmic stablecoins came to be known for displaying erratic tendencies and unpredictability, thus falling short of everyone’s expectations.

By the end of this article, you will have an in-depth understanding of the fundamentals of algorithmic stablecoins, their mechanism, and why algorithmic stablecoins are so fragile. We will break up and simplify the concept so a beginner can understand it.

Algorithmic Stablecoins: A Beginners Guide
Algorithmic Stablecoins: A Beginners Guide. Image by Freepik

 What are Algorithmic Stablecoins?

Algorithmic stablecoins are a type of stablecoins that maintain stable prices using algorithms. 

The algorithm works on two coins(stablecoin and another cryptocurrency) and links them where one coin is used to absorb market volatility while the other works to keep the peg. The algorithm adjusts the prices depending on the supply and demand of investors.

Although algorithmic stablecoins are pegged to the value of a real-world asset, they are not backed by it. Some algorithmic stablecoins, like FRAX, are based on a hybrid fractional model which uses some form of collateralization.

Types of Algorithmic Stablecoins and How Do They Work?

Algorithmic stablecoins can be classified on the mechanism they work to keep their value. They can be broadly classified as rebase, seigniorage, and hybrid fractional model.

Rebasing Algorithmic Stablecoins

In rebasing stablecoins, the total supply of the stablecoin is not fixed, and the algorithm adjusts the coin base’s circulation based on demand and supply. The algorithm hurls into action when the price falls below a certain level and decreases the supply of the coins. Additionally, if the coin’s value exceeds a dollar, it mints more coins. Ampleforth protocol (AMPL) uses rebasing feature.

Seigniorage Algorithmic Stablecoins

Seigniorage stablecoins use a multi-asset system: volatile assets(collateral) and stablecoins. Through market arbitrage, minting and burning, and network demand, the stablecoin is stabilized by the volatile asset.UST and TerraLUNA are an example of Seigniorage Algorithmic Stablecoins.

Fractional Algorithmic Stablecoins

Fractional algorithmic stablecoins combine both algorithm and collateralization to offer an optimal solution. Unlike traditional unbacked algorithmic stablecoins, these stablecoins utilize a collateralization ratio to select the appropriate mix of collateral (such as USDC) and volatile crypto assets.  

It is the first stablecoin to implement partial collateral, which uses FRAX-pegged stablecoins and FXS-pegged governance tokens.

Why Do Algorithmic Stablecoins Fail?

By design, algorithmic stablecoins are fragile. Algorithmic stablecoins rely on two tokens – one stablecoin and another cryptocurrency that backs the stablecoin with a smart contract executing a relationship between the two.

Cryptocurrency, like all assets, moves up and down depending on demand and supply market conditions. When the demand for an asset with fewer supply increases, the price of the asset increases. Algorithms are supposed to keep this under control through their financial engineering and market incentives to attempt to peg a reference asset’s price, which is not very stable and is always susceptible to depricing.

However, these stablecoins often fail due to various factors such as market volatility, liquidity issues, and the inability of the algorithm to respond to unexpected events.

Let’s see what went wrong for TerraLuna.

What Happened with TerraLUNA?

The TerraLUNA crash occurred in MAY 2022 and affected the cryptocurrency Luna and its stable coin UST. So why did TerraLUNA crash? 

TerraUSD(UST) maintained its 1:1 parity with the US Dollar through an algorithmic relationship with Terra’s native cryptocurrency, LUNA. Let’s elaborate on it further. 

In the days before the collapse, over $2 billion worth of UST was unstaked (taken out of Anchor Protocol). Furthermore, hundreds of millions of them were immediately sold. There is debate over whether this action was a reaction to a particularly volatile period, rising interest rates in the broader cryptocurrency market, or a malicious attack on Terra’s ecosystem.

However, this severe selling pressure initially drove the price of UST below 91 cents. As expected, the currency has hit bottom and is trading well below a penny. While this occurred, LUNA’s market cap continued to fall relative to UST’s, further escalating the already high average redemption price of LUNA for UST and the implications that come with it.

Risks and Limitations of algorithmic stablecoins

Algorithmic stablecoins have an innately weak framework due to their uncollateralized assets, which use algorithms for pegging the price of a reference asset that is not stable and is prone to de-pegging risk.

If the algorithmic stablecoin doesn’t have the right balance between liquidity, incentives, and automation, then the coin can enter a “death loop” that makes peg restoration impossible.

Future of Algorithmic Stablecoins

Several lawmakers have voiced concerns about imposing stricter rules around algo-stablecoin regulations following UST’s collapse, which could lead to a bearish outlook for algorithmic stablecoins. The news of Canada banning algorithmic stablecoins further takes away public confidence and puts a big question about the future of algorithmic stablecoins.

The catastrophic failure of the uncollateralized algo-stable experiment, as seen in the collapse of UST, has dealt a severe blow to the credibility of algorithmic stablecoins. The loss of public trust in this mechanism poses a daunting challenge for any further attempts to restore confidence.

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Megha Kumar

Megha Kumar is a seasoned finance expert with a decade old experience in the banking industry. With a keen interest in the disruptive potential of Web3 and Crypto, Megha brings a wealth of knowledge and insights to her engaging articles on these subjects. In her free time, you can find her on Discord and listening to music.

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Megha Kumar
Megha Kumar is a seasoned finance expert with a decade old experience in the banking industry. With a keen interest in the disruptive potential of Web3 and Crypto, Megha brings a wealth of knowledge and insights to her engaging articles on these subjects. In her free time, you can find her on Discord and listening to music.