As cryptocurrency works its way toward mass adoption, one of its biggest obstacles is volatility. Markets change overnight, and we’re so desensitized to it that we don’t even blink at dips that would bring national economies to their knees. Stablecoins – cryptocurrencies that are tied to existing currencies or commodities like the US dollar or gold – are fungible, easily traded assets that bring us one step closer to mass adoption.
While promises of $1 = 1 coin make stablecoins appear simple, stablecoin projects rely on complex algorithms to do the following:
- Keep the currency’s value from inflating, (e.g., increasing over a dollar)
- Keep the currency’s value from deflating (e.g., dropping below a dollar).
- Keep the network informed of the exchange rate between the pegged asset and the stablecoin.
Writing this down as a three-step list is easy. Actually achieving it is not. It will take a truly well-structured stablecoin model to do this consistently over time, and so far, results have been experimental at best.
Tether, Digix, NuBits, and BitShares offer lessons on stablecoin models, but this article is focused on four stablecoins which recently received funding and are being actively developed:
|Stablecoin||Amount Raised in ICO or Seed Funding|
|Basis (formerly Basecoin)||$133 million|
Three Models for Stablecoins
Stablecoins come in three types: cash-collateralized, on-chain collateralized, and elastic. Depending on the model, these projects can be decentralized, collateralized, and efficient at stabilizing. As of yet, there hasn’t been a stablecoin able to achieve all three aspects, and doing so may not be necessary for one to succeed at maintaining a consistent value.
Cash Collateralized: Tether and NuBits are the classic one-to-one USD cash-collateralized stablecoins, as both earn their value from being backed by cash (though Tether appears to be headed towards an FCC audit to prove its worth).
Cash collateralized models are efficient to manage, but they’re also centralized. The advantages of cash collateralized stablecoins are their efficiency to stabilize and the fact that they are backed by concrete assets. Their centralized design makes them vulnerable to mistakes or intentional inflation by coin makers. We have to put a lot of trust in such developers, and many crypto enthusiasts point to this as being a central point of failure, bolstered by accusations of Tether inflating its value. This explains why cash collateralized models lack the trust needed to become true solutions to volatility in the crypto market.
On-Chain Collateralized: On-chain collateralized projects use a decentralized cryptocurrency to maintain a stable value. MakerDAO, for example, uses a DAO model to ensure buys and sells of the Dai fix the latter’s value at the equivalent of $1. On-chain models are vulnerable to market dips, or in extreme cases, black swan events, where currencies take a steep nosedive and all assets’ values become low.
BitShares is the original project from Daniel Larimer, the co-founder of Steem and the CTO of EOS. BitShares is the most successful example of an on-chain collateralized stablecoin. It was the first blockchain to scale to tens of thousands of transactions per second (TPS).
BitShares was Larimer’s response to centralized exchanges that are vulnerable to government regulatory shutdowns that interfere with the supply of cryptocurrencies. He created Bitshares, an early decentralized exchange with a cryptocurrency pegged to assets including the US dollar, gold, and the yen. While BitShares exists today, Larimer left the project because of its lack of customization to build EOS.
MakerDAO (DAI, MKR)
Today, MakerDAO, headed by Rune Christiansen, is the new leading experiment in on-chain collateralization. MakerDAO works by stabilizing pricing of its stablecoin, the Dai, with a governance coin, MKR, managing the collateralization of assets. MakerDAO has been thoroughly audited by an outside security firm, but it’s not without growing pains. In the case of a black swan event, MakerDAO would sell off MKR to restore the value of the Dai to one dollar. While its founder is confident the Dai can handle such extremes, we have yet to see this level of volatility.
Elastic: Basis (formerly Basecoin), Carbon, and Fragments all follow an elastic stability model. While both cash-collateralized and on-chain collateralized models buy and sell off either fiat cash, gold, or crypto assets to achieve a stable value, elastic coin supplies are fundamentally quite different. By increasing the supply of coins when prices are too high and shrinking it when prices go too low, elastic models reflect how modern governments control their monetary supply. Like the on-chain model, elastic models are decentralized, and like cash collateralized models, they are highly efficient at achieving stability.
Basis works like the Fed, by adjusting the supply of coins over time so its stablecoin always trades for $1. Basis is formed on the basis of the Quantity Theory of Money, which states that contracting and expanding the supply of money keeps prices and values stable. According to its whitepaper, “long-run prices in an economy are proportional to the total supply of money in circulation.”
Basis is a multi-asset currency which incorporates the Basis token (its stablecoin), bond tokens, and Base shares. Basis uses aggregate demand to decide how many Basis tokens should be in circulation based on its price relative to the dollar:
demand = (coin price) * (number of coins in circulation)
Basis uses an oracle system to track its price and adjust it, based upon whether the token’s value is too high or too low. If the price is over $1, Basis distributes more Basis tokens to its holders as well as Bond and Base Shares. When the price is under $1, the protocol creates and sells bond tokens, allowing buyers to purchase them for less than a dollar each as a speculation to hold and sell at a 1:1 ratio with Basis tokens, destroying excess Basis tokens in exchange for a later potential profit on bond tokens.
There’s obviously more complexity to this process, but there still remains the question as to how quickly these price adjustments would adjust value without any currency used as a backup. The Basis protocol relies heavily on the idea that bond buyers willing to speculate on Basis’ value rising will continually create liquidity in the market.
Carbon monitors its price much like Basis, but it determines how to adjust its token supply every 24 hours, using a distributed consensus model. Instead of running on a blockchain, Carbon has partnered with Hedera Hashgraph to run on the latter’s DAG to achieve a faster throughput compared to blockchain-based stablecoins. Carbon is using a proprietary distributed oracle model along with a graph-based platform, setting their approach apart from other elastic stablecoins.
Carbon holders act as nodes, voting on how to adjust its price weighted by collateral they supply in Carbon (CUSD) tokens. This model rewards bidders in the median with the collateral from those who voted under the 25th percentile and above the 75th percentile.
Carbon utilizes the Aztec Model to maintain the liquidity it needs to keep the Carbon stablecoin close to $1 in value. Members who help the system contract supply during periods of inflation receive a distribution of 100% of the upside profits in exchange for burning their tokens.
Carbon has a second coin called Carbon Credit that works as a cushion to absorb any volatility affecting its stablecoin, CUSD. Carbon’s model takes an adaptive, albeit more nuanced approach. Having Hashgraph as its consensus layer will give it unparalleled speed over other stablecoins, but it also leaves Carbon tethered to Hashgraph’s mainnet launch.
Fragments uses an elastic model tied to three different assets: reserves, bond tokens, and its stablecoin, USD Fragments. Though the Fragments team considers their project a low volatility cryptocurrency rather than a stablecoin, this model is similar to Basis in its setup.
Fragments employs an algorithmic monetary supply model to carry out the balancing needed to reduce volatility, with a focus on token holders. Token holders act as market makers, buying more Fragments when its price drops below a dollar, and selling when its price exceeds it. When market makers aren’t able to make up the price gap, an algorithmic buffer kicks in, autonomously buying and selling the reserve asset to bring Fragments closer to the $1 value.
By automating the buying and selling of tokens when needed, the supply of Fragments expands and contracts, in theory by enough to achieve its targets. These automated conversions remove some autonomy from token holders, who lose or gain a portion of funds when Fragments are converted into bonds and distributed to them proportionally to reduce the Fragment supply. When the price is too low, Fragments sells off reserves and distributes more tokens to wallet holders, increasing the total supply of Fragments.
Like Basis, Fragments follows a first-in, first-out model, rewarding token holders for burning Fragments into Bonds when it needs to contract supply, and receiving newly minted Fragments in exchange when deflation is needed. Fragments manages stability autonomously, providing the advantage of quick adaptability, but also removing decision-making from token holders when prices fall too far from the market maker layer. This leaves Fragments’ chances of success dependent upon people holding onto their tokens long-term.
Where We’re Headed
Stablecoins, along with cryptocurrencies in general, remain in the early stages, focused on finding mathematical or governance models to break economic barriers to stable payments. Though the elastic model appears to be the direction in which stablecoin development is going, there aren’t many teams working on them. That’s partially because cryptocurrency itself is still making its way through growing pains. While cryptocurrency remains a speculative market that’s full of volatility, the potential for large profits remains. Now is an ideal time to put in the groundwork to address this future need for a decentralized economy. If cryptocurrency has taught us anything, it’s that many ideas and iterations are necessary to find success.
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