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Authors: Alisa DiCaprio, Austen Appleby
A novel type of stablecoin lost its peg. We’ve seen slipped pegs before in everything from national currencies to money markets to stablecoins. The current situation is getting more attention because of several unusual features: it happened in the midst of a wider macroeconomic downturn that has hurt crypto, Terra had a relatively large market cap, and algorithmic stablecoins are not well understood.
The core problem is an ongoing confidence crisis related to the backing model of stablecoins. The main impacts will be that collateralization with independently valued assets will become more common, regulatory consolidation will come sooner, but innovations will continue. This signifies a change in direction, not a full system meltdown.
To understand the implications of Terra, let’s first put it in context.
Terra (UST) is a stablecoin, which is an instrument in the DeFi ecosystem. It was estimated to have a market cap of $18.5 billion before it lost the peg.
There are hundreds of stablecoins, which fall into two categories based on how they are backed. The first is collateralized stablecoins. These are primarily backed by some mix of cash and other financial instruments, often held at off-chain institutions. Examples include USDC ($49.2 billion market cap) and Tether ($75.6 billion market cap) both of which maintain a one-to-one peg with the US dollar. They are widely used with a variety of crypto transactions. DAI also falls into this category being fully backed by a combination of USDC and collateralized debt in ether.
A second type is more niche, and this is where UST falls. Algorithmic stablecoins are often paired to a specific cryptocurrency (in the case of UST, it was tied to Luna), and as a result, don’t have independent assets in reserve to back up their value. They maintain price stability by programmatically expanding or contracting the supply of the cryptocurrency to which they are tied. There are several models used by algorithmic stablecoins including partial-collateral, rebasing, and seniorage share. Terra used a seniorage shares model, which is also used by Basis cash. Because all of these models are untested, algorithmic stablecoins tend to be more volatile in practice than collateralized stablecoins.
Effectively, algorithmic stablecoins are an experiment to see if we can maintain supply and demand equilibrium with an equation. The particular challenge in Terra is that neither UST nor Luna holds any value independent of the other. This can result in a confidence crisis when investors decide that neither has value.
Stablecoins have three uses, but are primarily employed to facilitate crypto transactions. They provide liquidity and a safe-haven asset for traders. UST is used to exchange with Luna, and also for the 19.5% APY it earned on Terra’s lending protocol, anchor.
All stablecoins are pegged. This means that they are designed to keep a specific value in relation to some target. USDC is pegged to the US dollar, such that 1 USDC = $1. UST’s valuation was tied to the cryptocurrency Luna, such that 1 UST could buy $1 worth of Luna.
Currency pegging is a historical concept. Plenty of economies have currencies that are tied in varying degrees to other currencies like the US dollar or the Euro. This means the same thing—they have pledged to maintain their currency at parity with either a different national currency or basket of currencies. If you go to Panama, your $1 will always buy you 1 Panamanian Balboa, for example.
To maintain pegs, countries or companies have to participate in market operations. For a country, this means the central bank buys or sells its own currency on the open market. This can be expensive. The country needs to hold enough foreign exchange reserves to manage value. If the value of the country’s currency goes down—say if speculators have shorted the currency—they have to be ready to buy and sell foreign exchange at the exchange rate price.
Past examples show us that it is expensive to regain a peg. In May 2022 Hong Kong spent $722 million to defend its peg to the US dollar. In September 1992, the UK lost €3.3 billion and had to withdraw from the European Exchange Rate Mechanism when the Bank of England attempted, and failed, to maintain a quasi-peg against aggressive shorting by investor George Soros.
Previous examples of stablecoins losing their peg include: Tether in 2017 (price fell to $0.91, took 3 weeks to get back to $1), and Neutrino in April 2022 (still trying to regain peg). Valuation falling below pegs is not unusual among stablecoins, but it is more common among algorithmic stablecoins due to nervousness about the sector. When UST slipped below the peg, Terra’s algorithm worked as it was supposed to—by adjusting the value of Luna which it could buy. But investors were unwilling to arbitrage once they lost faith in the value of Luna.
There are three facts we can take away from past examples: it is costly to defend a peg, slipping may be due to either malicious actors or loss of faith, and pegs can recover.
The spillover effects from Terra to other stablecoins, crypto and CBDC come from:
For stablecoins generally, selloffs during this episode have been the result of twitchy investors who were spooked by the rapid losses in UST. CELO dipped to $0.96 but was back up to $0.99. Tether briefly lost its peg, and processed $3 billion in withdrawals.
There are two implications for the future of stablecoins. First, in the short term, collateralization as a stablecoin backing strategy will increase in popularity. Even UST moved in this direction earlier in the month as the pressure became clear. The LFG was established to begin buying bitcoin for collateral. This episode will tip the scales towards at least partial collateralization with independent assets in the future. Related to this, we can expect that the type of risk-loving investors that are interested in algorithmic stablecoins will use their governance rights to push them towards more resilient recovery mechanisms. A second trend will be to increase regulatory attention. The US Treasury Secretary has already called for regulation by the end of the year. Although, as always, it is unclear what this means.
The impact on crypto has been limited. The reason is users primarily invested in the Terra network to receive the 19.5% APY rather than using it for payments. Luna holders were not buying other crypto with it nor were they using it to pay for physical or digital products. 75% of UST’s circulating supply was locked up in Anchor simply earning yields. The channel of impact on crypto has been Terra’s rapid selloff of bitcoin reserves during their attempt to defend the peg. As Terra sold their bitcoin reserve, the volume contributed downward pressure on bitcoin that had been part of the slowdown in bitcoin and ether trading volumes that had been ongoing since December 2021.
Since CBDC will be a form of money backed by the state, it should not share the contagion effects of a loss in confidence in an algorithmic stablecoin. However, CBDC designers should be informed by UST in several ways. First, there is a question of whether a CBDC should be interest bearing. The Terra episode shows that interest is an excellent way to build a user base, but it suppressed activity beyond the currency pair. Second, CBDCs are exploring whether to separate minting and distribution activities. In Terra, the hard coded speed of minting contributed to the cascading problems. If CBDCs are going to be used as cash, the network protocol will likely acknowledge the possibility of downturns and unexpected demand. Third, financial stability remains a primary concern for central banks, so this provides a data point about the spillover channels of a digital currency. While Terra’s problems did not spill over in the real sector, if regulations do not follow, it is likely that this will happen in the future. Finally, the crash of UST illustrates the difficulty of maintaining value when a token is not universally redeemable. Some discussion around CBDC has been the possibility of adding conditions to transferability, which could in theory lead to situations where it would sometimes trade below its face value.
The bottom line is that it was important for DeFi to have this experience. Going forward, we’ll see more regulation, more (high quality) collateralization, and continued innovation.
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yt.innertube::requests | never | This cookie, set by YouTube, registers a unique ID to store data on what videos from YouTube the user has seen. |
Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet.
Cookie | Duration | Description |
---|---|---|
_an_uid | 7 days | No description available. |
_clck | 1 year | No description |
_clsk | 1 day | No description |
_hjSession_1646283 | 30 minutes | No description |
_hjSessionUser_1646283 | 1 year | No description |
_uetvid | 1 year 24 days | No description available. |
_uetvid | 1 year 24 days | No description available. |
_uetvid | 1 year 24 days | No description available. |
6suuid | 2 years | No description available. |
AnalyticsSyncHistory | 1 month | No description |
AnalyticsSyncHistory | 1 month | No description |
AnalyticsSyncHistory | 1 month | No description |
ARRAffinitySameSite | No description available. | |
ARRAffinitySameSite | 1 year | No description available. |
ARRAffinitySameSite | 1 year | No description available. |
bEkAYwpdRGcM | 1 day | No description |
bEkAYwpdRGcM | 1 day | No description |
bEkAYwpdRGcM | 1 day | No description |
BIGipServer~magenta~mag-c-http-http-xhttpd-http-xhttpd-http-80 | session | No description |
DCMpagecount | session | No description |
DCMpagecount | session | No description |
DCMpagecount | session | No description |
debug | never | No description available. |
gaCookie | 2 years | No description available. |
gaCookie_gid | 1 day | No description available. |
LEAfgqMGWpwUs | 1 day | No description |
LEAfgqMGWpwUs | 1 day | No description |
LEAfgqMGWpwUs | 1 day | No description |
li_gc | 2 years | No description |
li_gc | 2 years | No description |
li_gc | 2 years | No description |
lpv413292 | 30 minutes | No description |
lpv413292 | 30 minutes | No description |
lpv413292 | 30 minutes | No description |
m | 2 years | No description available. |
metrika_enabled | session | No description available. |
metrika_enabled | session | No description available. |
metrika_enabled | session | No description available. |
muc_ads | 2 years | No description |
SM | session | No description available. |
STYXKEY_formcomplete | 9 months 26 days | No description |
UserMatchHistory | 1 month | Linkedin - Used to track visitors on multiple websites, in order to present relevant advertisement based on the visitor's preferences. |
UserMatchHistory | 1 month | Linkedin - Used to track visitors on multiple websites, in order to present relevant advertisement based on the visitor's preferences. |
UserMatchHistory | 1 month | Linkedin - Used to track visitors on multiple websites, in order to present relevant advertisement based on the visitor's preferences. |
vary | 1 month | No description |
visitor_id413292 | 10 years | No description |
visitor_id413292 | 10 years | No description |
visitor_id413292 | 10 years | No description |
visitor_id413292-hash | 10 years | No description |
visitor_id413292-hash | 10 years | No description |
Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.
Cookie | Duration | Description |
---|---|---|
SRM_B | 1 year 24 days | Used by Microsoft Advertising as a unique ID for visitors. |