Algorithmic Stablecoins: A Failed Experiment in Stability

The 2008 financial crisis was a perfect example of how complex securitisation-driven financial products almost led to the collapse of the financial system.

In the crypto sector, there is a complex asset class known as algorithmic stablecoins that sent the sector reeling more than one time. Algorithmic stablecoins have opaque designs, they are inherently fragile, and operate in a vulnerable state by default. An algorithmic stablecoin has no true peg, like fiat-backed or commodity-backed stablecoins. The peg derives its value from the expectation of its future market value, often backed by a volatile and fragile cryptocurrency. If the incentive structure in an algorithmic stablecoin ecosystem breaks down, the entire ecosystem fails.

We need regulatory safeguards for all algorithmic stablecoins to ensure transparency, disclosure, and fail-safes for containment.

Stablecoins are cryptocurrency assets designed to maintain a stable value by pegging it to another asset, such as a fiat currency or a commodity. There are various types of stablecoins, including fiat-backed, commodity-backed, crypto-backed, and algorithmic. Algorithmic stablecoins use market incentives, automated smart contracts, and reserve token adjustments to maintain a stable peg without requiring conventional underlying assets. While algorithmic stablecoins have the purported benefit of automated operation and the ability to scale, they are also the most unstable and fragile type of stablecoin.

To date, over 20 stablecoins have failed - some went quietly, and others went out with a bang, like Terra’s “UST” one year ago. What most of the failed stablecoins have in common is that they relied on algorithmic mechanisms for stability.

Terra’s collapse was not the first time an algorithmic stablecoin failed catastrophically. About one year earlier, in June 2021, Iron Finance collapsed similarly.

Iron Finance was a decentralised, non-custodial ecosystem that offered a range of DeFi products, protocols, and use cases across multiple chains. The original system of Iron Finance aimed to create a stablecoin called “IRON” using a two-coin structure. The problem was that IRON was pegged to $1 without having the actual backing of $1. To mint each IRON stablecoin, 75 per cent of its value was locked in collateralised USDC, and 25 per cent was locked in Iron Finance’s own governance token called “TITAN”. Essentially, Iron Finance tried to create a dollar from seventy-five cents under the assumption that TITAN would not fall below a certain price. They relied on the underlying asset to remain stable or increase in price. TITAN had no backing and its value was solely determined in the secondary DeFi market.

The system eventually failed catastrophically when the value of TITAN fell sharply in the secondary market, triggering a cascade sell-off of TITAN. This caused the IRON token to lose its peg, leading to a resulting “death spiral.” The entire system was designed on the assumption that TITAN itself would remain stable, which proved flawed. This operating structure was fragile from inception, and the failure of its stablecoin was not comparable to a traditional bank run. That story sounds all too familiar to what happened with Terra’s UST in 2022, and just about any other failed algorithmic stablecoin.

One of the major misconceptions about algorithmic stablecoins is that they are like fractional reserve banking in traditional finance. However, this analogy is flawed. While banks also create money through fractional reserves and lending, they are backstopped by government depositary insurance. The banks are also heavily regulated, subject to premium payments and supervision and examination by regulators.

The key takeaways about algorithmic stables coins thought by the lessons of Iron Finance and Terra UST are that:

Any financial product that relies on a minimum level of demand for the product class to function is inherently fragile and failure-prone when demand dries up. A non-collateralised token that purports to be stable requires at least consistent or increasing demand. When the demand dries up, the peg fails.

Algorithmic stablecoins rely on market incentives to perform price-stabilising functions. Historically, these functions always fail in a crisis when they are needed the most.

Interconnectedness is bad when there is a bank run. Terra’s crash in 2022 was one of the key contributors to the sharp decline in the crypto sector during the spring of last year. When Terra crashed in a matter of days, large investment firms like Three Arrows Capital got squeezed on all illiquid positions. Three Arrows Capital was the first crypto investment firm to go bankrupt last year, many followed for the same reason. FTX blew up a couple of months later. The collapse of the crypto markets and exposure to failed firms were key contributors to the collapse of FTX.

Unlike collateralised stablecoins backed by fiat or commodities, algorithmic stablecoins will fail. Collateralised options have better designs and are more resistant to market volatility and periods of crisis. However, what all stablecoins have in common is the lack of transparency, safeguards, and regulatory oversight. Current regulations around stablecoins are fragmented and inefficient. There are many lessons available to regulators in creating a regulatory framework for stablecoins: issuer registration requirements, prudential rules, collateral safeguards, reporting, risk assessment and containment measures.