A Brief History of Algorithmic Stablecoins
This article is the first of a four-part series by YaaS Analytics discussing algorithmic stablecoins, with a focus on the UST crash and the resulting fallout. The series will consist of four articles:
1. A Brief History of Algorithmic Stablecoins
3. The Collapse of Terra Classic
4. The Aftermath of the UST Crash
With the recent implosion of Terra Luna, many in the industry are wondering what the future holds for algorithmic stablecoins. Instead of postulating about the future, this article will instead look back on the history of algorithmic stablecoins, examining previous failed algorithmic stablecoins and posing answers to some of the most important questions around algorithmic stablecoins today.
What is an Algorithmic Stablecoin?
Stablecoins and stable assets are some of the most interesting applications of blockchain technology, taking advantage of all the benefits of a cryptocurrency — decentralization, security, low transaction fees, no intermediaries — while avoiding the foremost downside; the price volatility. The hurdle to developing a successful stable asset lies in the price stabilization mechanism (also known as the pegging mechanism); how does the stable asset maintain its peg to the relevant reference asset?
In general, stable assets can be classified as either algorithmic or collateralized, both of which are targeting “stability”, but have extremely different use cases, functionality and fundamentals. As their name suggests, collateralized stable assets are backed entirely by collateral held in reserve. Collateralized stable assets aim to generate stability either by creating a tangible link to a “traditional stable asset” like a fiat dollar or a deposit in a bank, or by backing the stable asset with an “overcollateralized” amount of a less stable digital asset (like Ethereum) to absorb volatility. Stability is the goal at all costs and this lends these assets simplicity and greater utility in payments and other high growth use-cases of today. However these models are not necessarily the most capital efficient. This has led to a wide array of experimental stable models which seek to solve the stability vs. capital efficiency dichotomy. This article series will be focused on these algorithmic experiments, but we will return in a follow-up series to the here and now and dive into the leading collateralized stable assets including suggesting some alternative nomenclature to clearly delineate between the two.
An algorithmic stablecoin is a type of stablecoin that relies on a price stabilizing algorithm to maintain its peg to a reference asset, such as the US dollar. Algorithmic stablecoins are usually divided into seigniorage style, rebase style or fractional style algorithmic stablecoins.
A popular type of algorithmic stablecoin is the seigniorage style stablecoin. A true seigniorage stablecoin holds its peg strictly using algorithms without needing any collateral. This type of stablecoin typically consists of two tokens; the stablecoin and a second, free-floating cryptocurrency which is used to absorb the price volatility from the stablecoin by providing users with arbitrage opportunities; users can always buy or sell the stablecoin at its intended price, with payment being sent or received in the form of the second token. To illustrate this process, we will use a fictional stablecoin STBL, with a stabilizing cryptocurrency CRYP. Assume that STBL is pegged to the US dollar, such that one STBL token is worth $1USD.
- If STBL begins trading on the open market at $1.10USD, users will then be able to exchange their CRYP for STBL from the protocol at a rate of $1USD per STBL; this means that users will effectively be paying $1 for an asset worth $1.10. This in turn will expand the supply of STBL, as the protocol will mint more STBL to sell to users, which in turn will cause the price of STBL to drop back down to $1. The thought process behind this mechanism is that users will continue buying STBL until it is no longer profitable to do so — which will occur when the price of STBL reaches $1.
- The converse is true when the price of STBL on the open market drops below $1; say STBL is currently trading at $0.90. In this case, users will be able to sell their STBL back to the protocol in exchange for CRYP, at a rate of $1 per STBL. This means that users will be effectively paying $0.90 for an asset worth $1. The protocol then burns the STBL it buys back, thus decreasing the supply of STBL and in turn increasing the demand, causing the price to go back up.
Note the difference between the price of STBL on the open market and on the protocol; users can always buy STBL from the protocol at exactly its peg and then trade it on the open market at whatever its current price. In this way, the protocol functions similar to a central bank in traditional finance. This function is what allows for arbitrage opportunities. Figure 1 illustrates how this algorithm works to raise the price of STBL in the event that it should drop below its peg. The thinking is that in this case, users will continually trade in their STBL for $1 of CRYP for as long as it is profitable to do so — while STBL is trading at less than $1. Thus, this form of algorithmic stablecoin relies predominantly on its users to maintain the coins peg.
Why Algorithmic Stablecoins Have Failed Repeatedly
The value in stablecoins is based on their stability; if a stablecoin cannot consistently maintain its peg, it is no longer stable and will become worthless. Non-collateralized algorithmic stablecoins are inherently vulnerable in that the only thing maintaining their peg is an algorithm. According to a paper by Dr. Ryan Clements , there are three main factors that affect the efficiency of these algorithms and thus the stability of a stablecoin:
Market Demand & Volatility: As with any product, the conditions of the relevant market can have drastic effects on the efficiency of the product. Due to the implicit volatility of the crypto market, massive shifts in the market can occur in short periods of time. Algorithmic stablecoins rely on a specific level of demand to operate successfully. Support for financial products is not always guaranteed especially in volatile market conditions. While these algorithms can be stress tested, it is impossible to predict the entire range of responses the algorithm could have to different market conditions.
Independent Actors: Previous iterations of algorithmic stablecoins have relied on an even less predictable factor to maintain their peg; independent actors. Each of the aforementioned failed stablecoins relied on independent actors to maintain the stablecoins peg through providing these actors with monetary incentive through arbitrage opportunities. While it is usually safe to assume that users will act in a way that they believe will maximize their earnings based on the information available to them, human behavior is erratic and cannot be perfectly predicted. (Alternatively, humans will typically act in a way that they believe will maximize their own profits given the information available to them, and this does not always coincide with what will best help the protocol, ie death spirals).
Price Information: In order to provide people with these arbitrage opportunities, protocols also require fast, reliable and up-to-date price information such that people are appropriately incentivized to stabilize the coin’s price. While price oracles usually provide accurate price information, most report prices periodically instead of continuously. This leads to slight information delays which, in the event of a crash or attack, can prove catastrophic.
Thus, the three fundamental building blocks upon which stablecoins rely to maintain their peg — market conditions, independent actors, and quick, reliable price information — are all hard to predict and indeed ‘unstable’, and so the convergent reliance on these three factors leaves algorithmic stablecoins to be inherently vulnerable.
Previous Failed Algorithmic Stablecoins
Basis Cash (BAC): Basis Cash launched on Ethereum in December 2020, to much excitement and anticipation. However, despite the positive sentiment surrounding the stablecoin, BAC never managed to consistently maintain its peg to the US dollar, frequently experiencing wild fluctuations before crashing to $0.30USD by February 2021. BAC never recovered, and currently trades at $0.0061USD. Interestingly, it was recently reported by CoinDesk that Do Kwon, the founder of Terra, was also behind Basis Cash, with him and the other founders launching it under the pseudonyms “Rick” and “Morty”.
Empty Set Dollar (ESD): Empty Set Dollar was another seigniorage style algorithmic stablecoin launching in late 2020, reaching a peak market cap of $560mm in December 2020, before collapsing to $91mm a month later, and with a current market cap of $650k at the time of writing.
Iron Finance: Launched in June 2021, the Iron Finance stablecoin (IRON) was a stablecoin pegged to the US dollar, and was the first partially collateralized stablecoin. It was another example of a seigniorage style stablecoin, and was launched alongside a standard token, TITAN, which was used to absorb the price volatility of IRON. As investors bought into the protocol, the price of TITAN boomed, leading whales to sell their TITAN in droves in order to realize profits. This caused the price of TITAN to drop, leading smaller investors to begin panic selling, causing IRON to lose its peg and thus initiating a ‘death spiral’, in what is considered the first major bank run in crypto.
There were two main factors that enabled this collapse: Partial collateralization and the lack of a proper price stabilizing mechanism. Iron Finance was only partially collateralized, meaning that the value of the assets they had in reserve totaled less than the maximum possible loss by the protocol. This meant that when users began panic selling their IRON, the protocol did not have enough reserves to pay out all of its users, further fueling fear and perpetuating the death spiral. As well, the lack of a proper price stabilizing mechanism meant that Iron Finance relied on a price feed oracle to relay the price of the tokens so that the protocol could then provide the appropriate arbitrage opportunities. The issue here was that the price feed oracle relays prices periodically instead of instantaneously; this meant that once the price of TITAN started to collapse, the delay between the reported price and the actual price meant that arbitrage opportunities were unprofitable, thus disincentivizing users from stabilizing the price. Because Iron Finance relied on users to stabilize the price, this lack of incentive caused further sell-offs and accelerated the death spiral. While the price of IRON did eventually restabilize, it did so at an insignificant market cap, and the coin has since faded into obscurity.
Terra Luna: On May 7th, the algorithmic stablecoin UST lost its peg to the US dollar, in what is considered one of the biggest crashes in crypto. On the morning of May 7th, UST had a market cap of $18.79B USD; at the time of writing this figure currently stands at $84.58mm, marking a decrease of 99.5%. Luna (now Luna Classic — LUNC), the stabilizing coin to UST, dropped from a high of $73.89 on May 7th to a current price of $0.00005796, effectively a 100% decrease, and UST currently sits at $0.0423. In the next article, we will discuss the meteoric rise of UST, including the factors that led it to become one of the biggest stablecoins on the market.
A Light in the Dark
Despite the previous failed iterations, it’s not all doom and gloom for algorithmic stablecoins. At the time of writing, one example of an algorithmic stablecoin that has experienced continued success and stability is RAI, a decentralized and non-pegged stable asset launched in February 2021 by blockchain startup Reflexer Labs. Defined by RAI co-founder Ameen Soleimani, “RAI is an asset backed only by ETH, governance-minimized, and programmed to maintain its own price stability without needing to peg to an external price reference like the USD.” Thus far RAI has been generally successful in its endeavor for price stability, with the price fluctuating a maximum of ~5.5% over the last year, between $2.949 and $3.1152.
RAI relies entirely on an algorithm to maintain stability and has been designed such that minimal governance is required. It was initially launched with the goal of becoming an alternative to pegged stablecoins with use cases as collateral and as a stable reserve asset. RAI has both an open market price and a redemption price, the latter of which is the price at which RAI can be redeemed at in exchange for ETH. RAI achieves stability via an algorithmic controller that automatically sets a redemption rate on its tokens such that when the open market price of RAI deviates from its redemption price — in this case $3.14 — people are incentivized to return it to its target price. This algorithmic controller is called a PID Controller, which is a control loop mechanism that is widely used in industrial control systems amongst other things.
Why Do People Think Algorithmic Stablecoins Are the Holy Grail of Decentralized Finance
Algorithmic stablecoins have often been referred to as the ‘Holy Grail’ of decentralized finance. This is because a successful stablecoin has the potential to be incredibly capital efficient — they require very little capital relative to the potential value they can generate. Figure 2 shows the positioning of algorithmic stablecoins in the stablecoin trilemma. Algorithmic stablecoins are incredibly capital efficient due to the lack of collateralization — meaning that they require very little capital to produce $1 of value — as well as fully decentralized. Because of this, algorithmic stablecoins have huge potential, but for the reasons outlined above, many have thus far consistently failed when it comes to maintaining price stability. Despite this, they also have the greatest potential of achieving all three aspects of the trilemma. Fiat collateralized stablecoins are fundamentally centralized, as there must be a central entity controlling fiat reserves. Similarly, crypto collateralized stablecoins are fundamentally capital inefficient, as thus they must be overcollateralized due to the volatility of the crypto market. Algorithmic stablecoins on the other hand have the potential to achieve price stability — doing so just requires a working algorithm.
This fact in conjunction with the potential efficiencies of algorithmic stablecoins means that we have not seen the last of these digital assets, and the world will continue to come up with different iterations in search of a working algorithm.
Why Are Controls and Regulations So Important?
Controls and regulations are necessary in all kinds of finance, but particularly so in novel structures like decentralized finance as investor, user and ecosystem protection is paramount to a successful evolution of the economy. It is one thing for free floating assets like Bitcoin to have risk baked in since there is also price upside, but for assets designed on stability, there is only downside to the loss of stability and therefore risk tolerance on these assets is much lower. Countless stakeholders lost money on UST. Those who did not generally either had an inside view of events, a deep understanding of how algorithmic stablecoins were constructed, “machine speed” monitoring and alerts or were those who purchased decentralized insurance. An average user was woefully underprepared and undereducated for an event like this.
Experiments are important to allow as they are critical to the evolution of our industry and economy, but insider activity, appropriate disclosures and appropriate demonstrated risk-tolerance are even more important in nascent types of economic structures like algorithmic stablecoins. The meltdown of UST also had meaningful ripple effects with other major CeFi players suffering liquidity crunches and declaring bankruptcy. We will dig into these later in the story. Interestingly however, the majority of DeFi platforms have continued to operate as intended even in the period of significant volatility that preceded and followed the meltdown. This lends credibility to the algorithms that manage leverage, collateral and liquidation triggers on those platforms.
In all, it is a hallmark of all finance that we constantly are searching for a better mousetrap. Evolution will continue and in the open-source, decentralized world of digital assets evolution happens quickly and often explosively. It is critical to the founding ethos of the digital asset industry that regulation be smart and allow room for this type of radical experimentation with economic models. However, it is also exceedingly important that:
1. Stakeholders be given all the information they require to make fully informed decisions
2. Information asymmetry be kept to a minimum to reduce material differences in actionable intelligence by insiders and the broader stakeholder community
3. Bad actors continue to be prosecuted for fraud, insider trading, market manipulation, wash trading and other securities and criminal law violations
In our next article, we will take a look at the rise of Terra, including why UST became the 4th largest stablecoin on the market in just a year and a half, as well as the factors that led to this explosive growth.
By Jack McKay & Julie Paterson
