Stablecoins II
Key Takeaways
- Stablecoin projects need to find their killer utilities to maintain their pegs and ensure protocol longevity.
- Beyond utility, algo stables have attracted more attention and funding than their asset-backed counterparts due to their superior capital efficiency.
- Having laid out the different mechanisms through which stables maintain their peg, we examine stable projects who have significantly de-pegged, including Ichi, Iron Finance, and Beanstalk.
- From first principles, currencies fulfill three functions: (i) controlling supply, (ii) defending value, and (iii) maintaining peg. Currencies that try to achieve all three functions inevitably revert to the two more relevant ones.
- Bottom Line: While we have seen some breakthrough models in purely algo and fractional algo models, we remain cautiously optimistic that these protocols will further push their first-mover advantage to achieve lindy.
In the last issue of DeFi Digest, we discussed the need for stablecoins to have utility to maintain their pegs, before diving deep into the different kinds of stables. Now that we have a lay of the (stables) land, we extract essential lessons from stable projects which have come and gone before us. Then, we take a step back to examine the dilemma stablecoins face from first principles.
While the projects below may have different peg-maintaining characteristics and catalysts for their downfall, they have two things in common:
- Lack actual utility / demand for them apart from high Pool 2[1] yields
- Inflate beyond organic demand and suffer from the repercussions of such in a sharp irreparable downturn
Graveyard of Stablecoins Past
While asset-backed stables are relatively ‘safer’ than algo stables, they are not exempt from de-peg risk. Remember, utility is pivotal for stables to maintain their peg, arguably more so than the mechanisms through which stables maintain their pegs.
Having said that, more algo stables projects have been created (and failed) relative to asset-backed stables given their superior capital efficiency and experimental nature. This is because algo stables do not need to custody USD for every stablecoin minted.
Blinded by the promise of growth, however, many algo stables founders get ahead of themselves and inflate beyond organic demand. Doing so may seem relatively harmless during bull markets but may irreversibly de-peg algo stables during bear markets.
Ichi
Ichi prides itself on being the ‘currency for every community.’ It allows protocols (and communities) to lock their protocolTokens to mint oneTokens, their own ICHI-branded stablecoin. Essentially, Ichi is a self-service MakerDAO with self-branded DAI tokens minted using the protocol’s token as collateral.
oneTokens are fractionally algorithmic, requiring a mix of volatile (protocolTokens) and stables collateral (oneTokens). The stables collateral functions as the backstop to defend liquidity at peg and allows for oneTokens to be redeemed for more established stables (e.g. $DAI, $USDC, $USDT), while the volatile protocol token goes into the treasury governed by oneToken holders.
Coupled with high rewards on Pool 2 (protocolToken-oneToken) and Pool 3 (oneToken-$ICHI), this introduced several knock-on effects:
- Protocol token liquidity is reduced as a result of them being used as collateral
- Demand for protocolTokens and oneTokens increase to reap high APRs
- Peg remains strong as long as demand persists
Having the foresight of a potential death spiral, the Ichi team introduced ‘Angel Liquidity Vaults’, where ICHI and oneTokens are deposited in Uni v3 concentrated liquidity pools to establish liquidity directly under the price of the protocolToken.
Ichi uses Rari’s Fuse pools as the back-end for this, where protocols themselves (including Ichi) set parameters on these permissionless pools. ICHI decided on a Loan-to-Value (LTV) ratio of 85%, implying a 118% collateral ratio. This is relatively high for an unproven stablecoin. For reference, MakerDAO allows users to borrow c.60% DAI against (170% collateral ratio) their deposited ETH.
The strategy was lauded by many as it optimized liquidity right below the spot price to facilitate smoother selling when markets sour, while removing protocolToken liquidity for upward price pressure when markets improve.
As $ICHI is required in Pool 3 on Fuse, $ICHI appreciated from $20 to $145 over 1 month. Higher $ICHI price improved the collateral ratios of Pool 3, which begot more leverage.
The rehypothecation loop flooded the market with $ICHI and oneTokens, vastly outsizing the Uni v3 ‘buy wall’ promoted.
The deleveraging process is as elegant, if not more:
- Once early adopters start taking profits (selling $ICHI), $ICHI price reduces and APRs suppress
- $ICHI-denominated collateral depreciates, leaving lenders in Rari undercollateralized
- Loans are liquidated, selling $ICHI into thin liquidity that constitutes the ‘buy wall’
- As $ICHI heads to 0, impermanent loss reduces the value of oneTokens as well
- Users realizes there’s no reason to hold $ICHI and oneTokens other than APRs (which are now unattractive), further accelerating the death spiral
Alas, Ichi fell victim to mistaking liquidity for utility.
Iron Finance
Iron Finance was a stablecoin project originally forked from Frax Finance, partially collateralized by $USDC and partially algorithmic with $TITAN (similar to $FXS). Before the collapse, it touted “one of the best returns on stablecoin pairs” with $TITAN reward emissions in IRON/USDC and IRON/BUSD pools.
Minting the $IRON stablecoin required varying ratios of $USDC and $TITAN to be burned. This means that the influx of TVL chasing high yields created a flywheel effect of increasing demand for $IRON and burn for $TITAN, further increasing the yield of the pools.
At its peak, Iron Finance amassed over $2.3B in committed capital to back its $IRON stablecoin, even drawing endorsements by celebrities like Mark Cuban. Little did the crypto class of 2021 know that these high emissions represented a double-edged sword.
See, $IRON stablecoins had no use case outside the Iron Finance ecosystem – holders of $IRON simply held them because of high rewards in Pool 2 (e.g. IRON-USDC / IRON – USDT). The Iron Finance team tried to create ancillary use-cases for $IRON, but most of them possessed the same tactic – higher yields for $IRON token.
Then in June 2021, it experienced a bank run, resulting in the collapse of $TITAN from $64 to fractions of a penny. Instead of redeeming $IRON for $TITAN when $IRON was off-peg, whales removed liquidity from $IRON-$USDC LP, then selling $IRON to $USDC. Coupled with a 10min delay in oracle pricing, suddenly it became unprofitable for arbitrageurs to buy $IRON and redeem for $USDC and $TITAN for a profit, let alone trust that $IRON will regain its peg.
While $IRON has since done so (it took a few months), the black swan event has revealed the project’s design flaws, rendering it difficult for the project to attract demand for $IRON ex post facto.
Beanstalk
Beanstalk was a credit-based algo stable project built by an originally anonymous team. The project particularly resonated amongst the crypto natives because of its lack of traditional venture funding. At its peak, Beanstalk grew to almost $100m in market capitalization, attracting $144m in liquidity.
Like other algo stables, Beanstalk was designed to create buying pressure whenever $BEANS de-pegs to the downside (<$1) and sell pressure whenever $BEANS de-pegs to the upside (>$1).
The protocol does this by issuing debt (dubbed ‘Pods’) whenever $BEANS is trading below $1, promising to pay back creditors with more $BEANS in the future. This is akin to a traditional finance zero-coupon bond (ZCB) that pays out its par value at maturity. As of 3/17, users could purchase ‘Pods’ (ZCB) for $0.03 with a future par value of $1.00 (3,333% increase). The 6,987% APR implies a maturation date of 5.7 months, based on past volatility and peg history.
Conversely, when $BEANS trades above $1, the protocol repays their debt to Pod holders who previously underwrote $BEANS when $BEANS traded below $1. In the example above, Beanstalk ‘pays back’ the 599,999,999 Pod holders in chronological order before making good on Pod #600,000,000. At this point, Pod #600,000,000 will become one unit of $BEANS worth more than $1. This redemption mechanism increases the supply of $BEANS, pushing the price of $BEANS back to $1.
Pods can be traded on the native marketplace on Beanstalk, creating liquidity for users who need it before Pod (ZCB) maturity. Pods that are closer to maturity are worth more because they can be converted to $BEANS sooner, and vice versa.
Beanstalk differs from Ichi and Iron Finance in that its catalyst stemmed from a governance vulnerability. A malicious actor took out a flash loan[2] for $1b worth of stables to partially buy $BEANS, using it to create BEAN3CRV-f ($BEANS, $USDC, $USDT, $DAI) pool on Curve Finance) and a separate BEAN3LUSD ($BEANS, $LUSD) pool.
They then converted both pools into ‘Seeds,’ a governance asset on Beanstalk that was initially intended for progressive decentralization of the protocol. In the wrong hands, however, these Seeds were used to vote for proposals submitted by the attacker, namely to funnel c.25k ETH (worth $181m at the time of exploit) to their wallet and $250k $BEANS to the Ukraine donation address.
Although Beanstalk’s downfall came from a governance attack, $BEANS had no clear utility (apart from high APR) until the exploit. As Beanstalk is not backed by venture funding (unlike Wormhole), it needed to raise funds and build credibility amongst users. The team proceeded to self-doxx[3] and proposed a ‘Barn Raise’ to attract liquidity to revamp the project, the success of which remains to be seen.
The Death Spiral
Between the failed stable projects above, all of them experienced a pronounced ‘death spiral’. Ichi, Iron Finance and Beanstalk all saw $ICHI / oneTokens, $TITAN / $IRON and Pods / $BEANS values significantly decline. Coupled with delayed oracles in some cases, the devaluation of these ‘sharecoins’ disincentivizes arbitrageurs beyond repair.
Hence, leading algo stables such as Terra and Frax Finance attempt to control what we call ‘free circulating supply’[4] of their sharecoins ($LUNA and $FRAX), whilst maximizing liquidity as a proportion to circulating market cap. Lower circulating supply minimizes the amount of tokens for sale during market downturns, while high liquidity relative to market cap facilitates less volatile price fluctuations.
The oldest tricks to achieve this in this relatively new book include incentivizing users to borrow against them (borrow UST against bLUNA on Anchor) and offering high APRs (in sharecoins) to AMM liquidity pools.
These tactics merely buy time for stable projects to find product-market fit and their killer use cases in the space. On the flip side, the cost of buying time compounds for projects as more and more tokens get emitted with time.
What Roles does a Currency Fulfill?
To perceive money from first principles, we leverage the framework below presented by @mrinconcruz. Instead of perceiving money as units of account, store of value, and medium of exchange, an alternative is to focus on the roles that money fulfills. These roles are adaptive supply, durable value, and stable peg – different types of money fulfill different roles, but not all three.
In his words:
“Fiat money supports adaptive supply and value durability—these are good media of exchange.
Collateral money supports value durability and peg stability—these are good stores of value.
Rebasing money supports adaptive supply and peg stability—these are good units of account.”
Money that attempts to achieve all three functions (read: no clear mandate) has historically failed. Fiat money is a great illustration of this, the most of which is the Central Bank of Thailand’s decision to peg the Baht to the US Dollar. Between 1985 and 1996, Thai interest rates were on average 5% higher than its peers, attracting foreign direct investment (FDI) from abroad. To accommodate this, foreign reserves grew from $4b to $36b within 10 years.
Put simply, the Central Bank of Thailand undertook the mandate of manipulating supply to fix rates, maintaining the value of the Thai Baht for its citizens, and pegging the Thai Baht to the US Dollar. This unwinded during the Asian Financial Crisis when the Thai Baht lost its peg to the US Dollar and immortalized George Soros into the hedge fund giant he is today.
The framework works well with collateral money, too. Collateral money (e.g. DAI) can maintain value and a stable peg, but its supply is limited by the collateral (e.g. ETH) that is deposited to back the money. On the other hand, rebasing money regulates the total circulating supply (see: $AMPL) to maintain its peg, but does not prioritize value for a given holder.
Since money can only robustly support two of three functions in the ‘stables trilemma,’ those who try to fulfill all three functions inevitably have to snap back into two. Fiat money with non-free-floating exchange rates eventually reverts to pure fiat and gives up peg stability, often through painful devaluations. Collateral money with fractional reserves foregoes adaptive supply, while its experimental cousin rebasing money still needs to find product-market given its inability to maintain value.
While we credit Manny on his gigabrain[5] stables framework, we differ in thinking with regards to the sustainability of algo stables.
“In the case of the US, the dollar’s role as a reserve currency encouraged demand for dollar safe assets. Despite a declining US share of world GDP, self-reinforcing trade-settlement network effects, banking sector dollarization, and other forms of historical path dependence sustain this demand of dollar safe assets in what is sometimes called ‘dominant currency paradigm’…
…Digital assets are far more volatile than any traditional asset class or currency… To believe that three-function moneys, such as the latest generation of ‘algorithmic stablecoins,’ can ‘bootstrap’ or ‘grow’ their way out of these lasting design questions is wishful, if not outright foolish, thinking.”
We acknowledge that algo stables do bear resemblance to ZCB, and that CDP stables are backed by volatile assets. However, ruling out all ago stable designs because of their similarity to past failed projects is premature in our opinion. Prominent algo stables today are crafting their own killer utilities that go beyond ponzi-like APRs (see DeFi Digest 8). While the US Dollar dominates as a reserve currency today, we believe the game-theoristic dynamics between nations has the potential to change this in the future.
Perhaps such optimism constitutes wishful thinking, but what are we doing here if not to re-imagine a better and decentralized financial future through novel DeFi primitives?
Bottom Line
Stablecoin projects need to find their killer utilities to maintain their pegs and ensure protocol longevity. Beyond utility, algo stables have attracted more attention and funding than their asset-backed counterparts due to their superior capital efficiency. While we have seen some breakthrough models in purely algo and fractional algo models, we remain cautiously optimistic that these protocols will further push their first-mover advantage to achieve lindy.
Taking a step back, currencies fulfill three functions, namely (i) controlling supply, (ii) defending value, and (iii) maintaining peg. Currencies that have tried to achieve all three have perished in vain, urging current incumbents to lean in on the two that matter.
Editor’s Note: We would like to h/t @jonwu_ and @mrinconcruz for their thought leadership in the space. Their first-principles thinking on stablecoins and currencies as a whole has helped inform our take on this nascent space.
[1] Pool 2 refers to yield farming pools that require exposure to the governance / farm token that is being farmed. For example, MATIC-QUICK pool on Quickswap is a Pool 2 that earns $QUICK rewards.
[2] Flash loans are blockchain loans that get repaid within the same transaction. The loan is near risk-free because if the loan doesn’t get paid back, the transaction is invalid, costing gas fees only.
[3] Doxxing refers to revealing identifying information about an individual, usually by malicious actors. Self-doxxing is the act of doing so by the individual themselves.
[4] Circulating supply refers to the amount of coins circulating in the market and tradeable by the public. It is comparable to looking at shares readily available in the market (not held & locked by insiders, governments). ‘Freely circulating supply’ excludes tokens that are staked to secure the PoS network or in liquidity pools.
[5] Gigabrain is a slang adjective for individuals with high IQ. The opposite of gigabrain is smoothbrain.
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