Vitalik Buterin, the co-founder of Ethereum (ETH), has shared two thought experiments on how to evaluate whether an algorithmic stablecoin can maintain its stability.
Buterin shared this exclusive thought following the multi-billion dollar losses caused by the collapse of the Terra (LUNA) blockchain and its algorithmic stablecoin TerraUSD (UST).
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In a blog post on the 25th of May 2022, Buterin said, “What we need is not stablecoin boosterism or stablecoin doomerism, but rather a return to principles-based thinking…
“While there are plenty of automated stablecoin designs that are fundamentally flawed and doomed to collapse eventually, and plenty more that can survive theoretically but are highly risky, there are also many stablecoins that are highly robust in theory, and have survived extreme tests of crypto market conditions in practice.”
Buterin’s blog post is specifically focused on Reflexer’s fully Ethereum (ETH)-collateralized RAI stablecoin. This form of stablecoin is not pegged to the value of fiat currency but relies on algorithms to automatically set an interest rate to proportionally oppose price movements and incentivize users so that RAI can maintain its target price range.
According to the developer, it exemplifies the pure ‘ideal type’ of a collateralized automated stablecoin.” He said its model also enables users to extract their liquidity in Ethereum (ETH) if they totally lose faith in the stablecoin.
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In the blog post, Buterin Shares two thought experiments to evaluate the stability of an Algorithmic stablecoin.
Thought Experiment 1: Can the stablecoin wind down to zero users?
According to Ethereum co-founder, if the market activity for a stablecoin project drops to near zero, users should be able to extract the fair value of their liquidity out of the asset.
Describing how TerraUSD (UST) failed this test due to its structure, Buterin wrote:
“In Terra, the price of the volcoin (LUNA) comes from the expectation of fees from future activity in the system. So what happens if expected future activity drops to near-zero? The market cap of the volcoin drops until it becomes quite small relative to the stablecoin. At that point, the system becomes extremely fragile: only a small downward shock to demand for the stablecoin could lead to the targeting mechanism printing lots of volcoins, which causes the volcoin to hyperinflate, at which point the stablecoin too loses its value.”
He added that “First, the volcoin price drops. Then, the stablecoin starts to shake. The system attempts to shore up stablecoin demand by issuing more volcoins. With confidence in the system low, there are few buyers, so the volcoin price rapidly falls. Finally, once the volcoin price is near-zero, the stablecoin too collapses.”
Comparing the situation to RAI which is backed by ETH, Buterin stated that declining confidence in the stablecoin would not bring about a negative feedback loop between the two assets, which will lessen the chances of a broader collapse, unlike TerraUSD (UST).
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Thought experiment 2: Negative interest rates option required
Also, Ethereum co-founder thinks it’s important for an algorithmic stablecoin to have the ability to implement a negative interest rate when it’s tracking a basket of assets, a consumer price index, or some arbitrarily complex formula that grows by 20% per year
Buterin noted:
“Obviously, there is no genuine investment that can get anywhere close to 20% returns per year, and there is definitely no genuine investment that can keep increasing its return rate by 4% per year forever. But what happens if you try?”
He said only two outcomes can emerge in such a situation. Either the project “charges some kind of negative interest rate on holders that equilibrates to basically cancel out the USD-denominated growth rate built into the index.”
Or “It turns into a Ponzi, giving stablecoin holders amazing returns for some time until one day it suddenly collapses with a bang.”
In conclusion, the developer said the fact that an algorithmic stablecoin is able to handle the scenarios mentioned above does not make it safe.
“It could still be fragile for other reasons (e.g. insufficient collateral ratios), or have bugs or governance vulnerabilities. But steady-state and extreme-case soundness should always be one of the first things that we check for,” Vitalik Buterin added.
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